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#fields
In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers.

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Field (mathematics) - Wikipedia
Module-like[show] Module Group with operators Vector space Linear algebra Algebra-like[show] Algebra Associative Non-associative Composition algebra Lie algebra Graded Bialgebra v t e <span>In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers. A field is thus a fundamental algebraic structure, which is widely used in algebra, number theory and many other areas of mathematics. The best known fields are the field of rational




Flashcard 1739221241100

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#fields
Question
In mathematics, a [...] is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers.
Answer
field


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In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers.

Original toplevel document

Field (mathematics) - Wikipedia
Module-like[show] Module Group with operators Vector space Linear algebra Algebra-like[show] Algebra Associative Non-associative Composition algebra Lie algebra Graded Bialgebra v t e <span>In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers. A field is thus a fundamental algebraic structure, which is widely used in algebra, number theory and many other areas of mathematics. The best known fields are the field of rational







Flashcard 1739223600396

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#fields
Question
In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to [...]
Answer


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In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers.

Original toplevel document

Field (mathematics) - Wikipedia
Module-like[show] Module Group with operators Vector space Linear algebra Algebra-like[show] Algebra Associative Non-associative Composition algebra Lie algebra Graded Bialgebra v t e <span>In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers. A field is thus a fundamental algebraic structure, which is widely used in algebra, number theory and many other areas of mathematics. The best known fields are the field of rational







Flashcard 1755486227724

Tags
#optimization
Question

mathematical optimization selects a [...] (with regard to some criterion) from some set of available alternatives.

Answer
best element

there are many answers to this question, but this answer is more intuitive from the measure oriented perspective.


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mathematical optimization selects a best element (with regard to some criterion) from some set of available alternatives.

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Mathematical optimization - Wikipedia
+ 4. The global maximum at (x, y, z) = (0, 0, 4) is indicated by a blue dot. [imagelink] Nelder-Mead minimum search of Simionescu's function. Simplex vertices are ordered by their value, with 1 having the lowest (best) value. <span>In mathematics, computer science and operations research, mathematical optimization or mathematical programming, alternatively spelled optimisation, is the selection of a best element (with regard to some criterion) from some set of available alternatives. [1] In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the







Flashcard 1758223535372

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#fields
Question
In mathematics, a field is a set on which [...] are defined, and behave as when they are applied to rational and real numbers.


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In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers.

Original toplevel document

Field (mathematics) - Wikipedia
Module-like[show] Module Group with operators Vector space Linear algebra Algebra-like[show] Algebra Associative Non-associative Composition algebra Lie algebra Graded Bialgebra v t e <span>In mathematics, a field is a set on which addition, subtraction, multiplication, and division are defined, and behave as when they are applied to rational and real numbers. A field is thus a fundamental algebraic structure, which is widely used in algebra, number theory and many other areas of mathematics. The best known fields are the field of rational







#fields
The best known fields are the field of rational numbers and the field of real numbers.

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Field (mathematics) - Wikipedia
ltiplication, and division are defined, and behave as when they are applied to rational and real numbers. A field is thus a fundamental algebraic structure, which is widely used in algebra, number theory and many other areas of mathematics. <span>The best known fields are the field of rational numbers and the field of real numbers. The field of complex numbers is also widely used, not only in mathematics, but also in many areas of science and engineering. Many other fields, such as fields of rational functions, al




Flashcard 1758236118284

Tags
#fields
Question
The best known fields are the field of [...] and [...] .


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The best known fields are the field of rational numbers and the field of real numbers.

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Field (mathematics) - Wikipedia
ltiplication, and division are defined, and behave as when they are applied to rational and real numbers. A field is thus a fundamental algebraic structure, which is widely used in algebra, number theory and many other areas of mathematics. <span>The best known fields are the field of rational numbers and the field of real numbers. The field of complex numbers is also widely used, not only in mathematics, but also in many areas of science and engineering. Many other fields, such as fields of rational functions, al







Flashcard 1803451763980

Tags
#jaynes #plausible-reasoning
Question
Plausible reasoning aims to develope [...] principless for inference
Answer
general, consistent, and unambiguous

People refuse to adopt? giant cunts!


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Plausible reasoning aims to develope general, consistent, and unambiguous principless for inference

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Flashcard 1804564303116

Tags
#vim
Question
All find commands (search for character) can be followed [...] to go the the previous searched item

Answer
, (comma)


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All search commands can be followed , (comma) to go the the previous searched item

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A Great Vim Cheat Sheet
er the cursor F [char] - Move to the next char on the current line before the cursor t [char] - Move to before the next char on the current line after the cursor T [char] - Move to before the next char on the current line before the cursor <span>All these commands can be followed by ; (semicolon) to go to the next searched item, and , (comma) to go the the previous searched item ##Insert/Appending/Editing Text Results in insert mode i - start insert mode at cursor I - insert at the beginning of the line a - append after the cursor A -







Flashcard 2961744465164

Tags
#best-practice #stan
Question
One of the most powerful means of validating a statistical algorithm is to verify that you can [...].
Answer
recover the ground truth from simulated data


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One of the most powerful means of validating a statistical algorithm is to verify that you can recover the ground truth from simulated data.

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Stan Best Practices · stan-dev/stan Wiki · GitHub
m used in Stan. All of these criteria are necessary but not sufficient conditions for a good fit -- in other words they all identify problems that will ensure a bad fit but none of them can guarantee a good fit. Recover simulated values <span>One of the most powerful means of validating a statistical algorithm is to verify that you can recover the ground truth from simulated data. Begin by selecting reasonable "true" values for each of your parameters, simulating data according to your model, and then trying to fit your model with the simulated data.







Flashcard 2961898867980

Tags
#PATH
Question
the $PATH variable are usually set in[...files...]
Answer
shell.dotfiles

~/.zshrc
or ~/.bashrc,or ~/.bash_profile


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Well, first make sure your $PATH variable is doing what you want it to. You likely have a startup script called something like ~/.bash_profile or ~/.bashrc that sets this $PATH variable.

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Running Jupyter with multiple Python and IPython paths - Stack Overflow
s that the packages you can import when running python are entirely separate from the packages you can import when running ipython or a Jupyter notebook: you're using two completely independent Python installations. So how to fix this? <span>Well, first make sure your $PATH variable is doing what you want it to. You likely have a startup script called something like ~/.bash_profile or ~/.bashrc that sets this $PATH variable. On Windows, you can modify the user specific environment variables. You can manually modify that if you want your system to search things in a different order. When you first install an







Flashcard 2965739539724

[unknown IMAGE 2965735083276]
Tags
#has-images #mapping
Question
a [...] function is a one-to-one and onto mapping of a set X to a set Y.
Answer
bijective


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a bijective function a one-to-one and onto (surjective) mapping of a set X to a set Y.

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Bijection - Wikipedia
nction between the elements of two sets, where each element of one set is paired with exactly one element of the other set, and each element of the other set is paired with exactly one element of the first set. There are no unpaired elements. <span>In mathematical terms, a bijective function f: X → Y is a one-to-one (injective) and onto (surjective) mapping of a set X to a set Y. A bijection from the set X to the set Y has an inverse function from Y to X. If X and Y are finite sets, then the existence of a bijection means they have the same number of elements.







#sets #sigma-algebra

the relative complement of A in B is the set of elements in B but not in A .

The relative complement of \({\displaystyle B\cap A^{\complement }=B\setminus A}\)

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Flashcard 2965984644364

[unknown IMAGE 2965987265804]
Tags
#has-images #sets #sigma-algebra
Question

the relative complement of A in B is the set of elements [...] .

Answer
in B but not in A

\({\displaystyle B\cap A^{\complement }=B\setminus A}\)


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the relative complement of A in B is the set of elements in B but not in A . The relative complement of \({\displaystyle B\cap A^{\complement }=B\setminus A}\)

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Complement (set theory) - Wikipedia







#probability-theory
If \({\mathcal {F}}\,\) is the Borel σ-algebra on the set of real numbers, then there is a unique probability measure on \({\mathcal {F}}\,\) for any cdf, and vice versa.

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Probability theory - Wikipedia
{\displaystyle {\mathcal {F}}\,} is called a probability measure if P ( Ω ) = 1. {\displaystyle P(\Omega )=1.\,} <span>If F {\displaystyle {\mathcal {F}}\,} is the Borel σ-algebra on the set of real numbers, then there is a unique probability measure on F {\displaystyle {\mathcal {F}}\,} for any cdf, and vice versa. The measure corresponding to a cdf is said to be induced by the cdf. This measure coincides with the pmf for discrete variables and pdf for continuous variables, making the measure-theo




Flashcard 2965992770828

Tags
#probability-theory
Question
If \({\mathcal {F}}\,\) is [...], then there is a unique probability measure on \({\mathcal {F}}\,\) for any cdf, and vice versa.
Answer
the Borel σ-algebra on the set of real numbers


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If \({\mathcal {F}}\,\) is the Borel σ-algebra on the set of real numbers, then there is a unique probability measure on \({\mathcal {F}}\,\) for any cdf, and vice versa.

Original toplevel document

Probability theory - Wikipedia
{\displaystyle {\mathcal {F}}\,} is called a probability measure if P ( Ω ) = 1. {\displaystyle P(\Omega )=1.\,} <span>If F {\displaystyle {\mathcal {F}}\,} is the Borel σ-algebra on the set of real numbers, then there is a unique probability measure on F {\displaystyle {\mathcal {F}}\,} for any cdf, and vice versa. The measure corresponding to a cdf is said to be induced by the cdf. This measure coincides with the pmf for discrete variables and pdf for continuous variables, making the measure-theo







#platão #timeu
Segundo Platão/Sócrates, todo o ato intelectual só pode ter lugar na alma

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Flashcard 2976165530892

Tags
#platão #timeu
Question
Segundo Platão/Sócrates, todo o ato intelectual só pode ter lugar na [local]
Answer
alma


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Segundo Platão/Sócrates, todo o ato intelectual só pode ter lugar na alma

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Tendo construído os corpos de cada um deles – sete ao todo –, o deus estabeleceu-os nas órbitas que o percurso do Outro seguia, em número de sete delas: na primeira a Lua, à volta da Terra; na segunda o Sol, por cima da Terra 125 ; a Estrela da Manhã 126 e o astro que dizem ser consagrado a Hermes 127 na rota circular 128 que tem a mesma velocidade que o Sol, ainda que lhes tenha cabido em sorte um ímpeto contrário ao dele

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Quanto à Terra, o nosso sustento, a qual roda 136 em torno do eixo que atravessa o universo, foi estabelecida como guardiã e produtora da noite e do dia; ela que é a primeira e a mais velha das divindades geradas dentro do céu

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De Geia e Urano foram gerados Oceano e Tétis, seus filhos, e destes foram gerados Fórcis, Cronos e Reia, e todos aqueles que os seguiram; de Cronos e de Reia foram gerados Zeus e Hera e todos aqueles que, segundo a tradição, sabemos serem seus irmãos, e ainda outros descendentes destes foram gerados.

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formai e engendrai seres-vivos, fazei-os crescer, providenciando- lhes o alimento, e, quando perecerem, recebei-os outra vez.

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Uma vez acalmados, gera-se o sossego, e, uma vez gerado um sossego profundo, abate-se um sono com poucos sonhos; mas quando restam alguns movimentos fortes, conforme a sua natureza e os locais onde ficam, produzem no interior simulacros que se assemelham, quanto à natureza e ao número, ao exterior e que serão recordados ao acordar.

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#best-practice #git
You should only commit code when it’s completed.

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Version Control Best Practices
or everyone to integrate changes regularly and avoid having merge conflicts. Having few large commits and sharing them rarely, in contrast, makes it hard both to solve conflicts and to comprehend what happened. Don’t Commit Half-Done Work <span>You should only commit code when it’s completed. This doesn’t mean you have to complete a whole, large feature before committing. Quite the contrary: split the feature’s implementation into logical chunks and remember to commit early




#best-practice #git
Resist the temptation to commit something that you “think” is completed. Test it thoroughly to make sure it really is completed and has no side effects

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Version Control Best Practices
ry before leaving the office at the end of the day. If you’re tempted to commit just because you need a clean working copy (to check out a branch, pull in changes, etc.) consider using Git’s “Stash” feature instead. Test Before You Commit <span>Resist the temptation to commit something that you “think” is completed. Test it thoroughly to make sure it really is completed and has no side effects (as far as one can tell). While committing half-baked things in your local repository only requires you to forgive yourself, having your code tested is even more important when it comes




#best-practice #git
writing commit messages with the imperative, present tense to be consistent with generated messages from commands like git merge.

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Version Control Best Practices
. Separate it from the following body by including a blank line. The body of your message should provide detailed answers to the following questions: What was the motivation for the change? How does it differ from the previous implementation? <span>Use the imperative, present tense („change“, not „changed“ or „changes“) to be consistent with generated messages from commands like git merge. Version Control is not a Backup System Having your files backed up on a remote server is a nice side effect of having a version control system. But you should not use your VCS like




#best-practice #git
The body of your commit message should provide motivation to and difference of the commit.

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Version Control Best Practices
n it comes to pushing / sharing your code with others. Write Good Commit Messages Begin your message with a short summary of your changes (up to 50 characters as a guideline). Separate it from the following body by including a blank line. <span>The body of your message should provide detailed answers to the following questions: What was the motivation for the change? How does it differ from the previous implementation? Use the imperative, present tense („change“, not „changed“ or „changes“) to be consistent with generated messages from commands like git merge. Version Control is not a Backup System




#best-practice #git
Branches are the perfect tool to help you avoid mixing up different lines of development

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Version Control Best Practices
mitting semantically (see “related changes”) – you shouldn’t just cram in files. Use Branches Branching is one of Git’s most powerful features – and this is not by accident: quick and easy branching was a central requirement from day one. <span>Branches are the perfect tool to help you avoid mixing up different lines of development. You should use branches extensively in your development workflows: for new features, bug fixes, experiments, ideas… Agree on a Workflow Git lets you pick from a lot of different wo




#best-practice #git
Branches

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Branches are the perfect tool to help you avoid mixing up different lines of development

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Version Control Best Practices
mitting semantically (see “related changes”) – you shouldn’t just cram in files. Use Branches Branching is one of Git’s most powerful features – and this is not by accident: quick and easy branching was a central requirement from day one. <span>Branches are the perfect tool to help you avoid mixing up different lines of development. You should use branches extensively in your development workflows: for new features, bug fixes, experiments, ideas… Agree on a Workflow Git lets you pick from a lot of different wo




Flashcard 2976208260364

Tags
#best-practice #git
Question
writing commit messages with the [...mood and tense...] to be consistent with generated messages from commands like git merge.
Answer
imperative, present


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writing commit messages with the imperative, present tense to be consistent with generated messages from commands like git merge.

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Version Control Best Practices
. Separate it from the following body by including a blank line. The body of your message should provide detailed answers to the following questions: What was the motivation for the change? How does it differ from the previous implementation? <span>Use the imperative, present tense („change“, not „changed“ or „changes“) to be consistent with generated messages from commands like git merge. Version Control is not a Backup System Having your files backed up on a remote server is a nice side effect of having a version control system. But you should not use your VCS like







Flashcard 2976210619660

Tags
#best-practice #git
Question
The body of your commit message should provide the [... and ...] of the commit.
Answer
Why and what

motivation and difference


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The body of your commit message should provide motivation to and difference of the commit.

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Version Control Best Practices
n it comes to pushing / sharing your code with others. Write Good Commit Messages Begin your message with a short summary of your changes (up to 50 characters as a guideline). Separate it from the following body by including a blank line. <span>The body of your message should provide detailed answers to the following questions: What was the motivation for the change? How does it differ from the previous implementation? Use the imperative, present tense („change“, not „changed“ or „changes“) to be consistent with generated messages from commands like git merge. Version Control is not a Backup System







Flashcard 2976212978956

Tags
#best-practice #git
Question
Resist the temptation to commit something that [...]. Test it thoroughly to make sure it really is completed and has no side effects
Answer
you “think” is completed


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Resist the temptation to commit something that you “think” is completed. Test it thoroughly to make sure it really is completed and has no side effects

Original toplevel document

Version Control Best Practices
ry before leaving the office at the end of the day. If you’re tempted to commit just because you need a clean working copy (to check out a branch, pull in changes, etc.) consider using Git’s “Stash” feature instead. Test Before You Commit <span>Resist the temptation to commit something that you “think” is completed. Test it thoroughly to make sure it really is completed and has no side effects (as far as one can tell). While committing half-baked things in your local repository only requires you to forgive yourself, having your code tested is even more important when it comes







Flashcard 2976214551820

Tags
#best-practice #git
Question
[...] before commit to make sure it is completed and has no side effects
Answer
Test it thoroughly


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Resist the temptation to commit something that you “think” is completed. Test it thoroughly to make sure it really is completed and has no side effects

Original toplevel document

Version Control Best Practices
ry before leaving the office at the end of the day. If you’re tempted to commit just because you need a clean working copy (to check out a branch, pull in changes, etc.) consider using Git’s “Stash” feature instead. Test Before You Commit <span>Resist the temptation to commit something that you “think” is completed. Test it thoroughly to make sure it really is completed and has no side effects (as far as one can tell). While committing half-baked things in your local repository only requires you to forgive yourself, having your code tested is even more important when it comes







Flashcard 2976216911116

Tags
#best-practice #git
Question
You should only commit code when [...].
Answer
it’s completed


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You should only commit code when it’s completed.

Original toplevel document

Version Control Best Practices
or everyone to integrate changes regularly and avoid having merge conflicts. Having few large commits and sharing them rarely, in contrast, makes it hard both to solve conflicts and to comprehend what happened. Don’t Commit Half-Done Work <span>You should only commit code when it’s completed. This doesn’t mean you have to complete a whole, large feature before committing. Quite the contrary: split the feature’s implementation into logical chunks and remember to commit early







#borel-algebra #measure-theory
In contrast to Borel sets, an example of a non-measurable set cannot be exhibited, though its existence can be proved.

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Borel set - Wikipedia
ery probability measure on a standard Borel space turns it into a standard probability space. Non-Borel sets[edit] An example of a subset of the reals which is non-Borel, due to Lusin [4] (see Sect. 62, pages 76–78), is described below. <span>In contrast, an example of a non-measurable set cannot be exhibited, though its existence can be proved. Every irrational number has a unique representation by an infinite continued fraction x = a 0




Flashcard 2976226610444

Tags
#borel-algebra #measure-theory
Question
In contrast to Borel sets, an example of a [...] cannot be exhibited, though its existence can be proved.


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In contrast to Borel sets, an example of a non-measurable set cannot be exhibited, though its existence can be proved.

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Borel set - Wikipedia
ery probability measure on a standard Borel space turns it into a standard probability space. Non-Borel sets[edit] An example of a subset of the reals which is non-Borel, due to Lusin [4] (see Sect. 62, pages 76–78), is described below. <span>In contrast, an example of a non-measurable set cannot be exhibited, though its existence can be proved. Every irrational number has a unique representation by an infinite continued fraction x = a 0







#probability-theory
The raison d'être of the measure-theoretic treatment of probability is that it unifies the discrete and the continuous cases, and makes the difference a question of which measure is used.

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Probability theory - Wikipedia
R n {\displaystyle \mathbb {R} ^{n}} and other continuous sample spaces. Measure-theoretic probability theory[edit] <span>The raison d'être of the measure-theoretic treatment of probability is that it unifies the discrete and the continuous cases, and makes the difference a question of which measure is used. Furthermore, it covers distributions that are neither discrete nor continuous nor mixtures of the two. An example of such distributions could be a mix of discrete and continuous distr




#probability-theory
to study Brownian motion, probability is defined on a space of functions.

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Probability theory - Wikipedia
rk on probabilities outside R n {\displaystyle \mathbb {R} ^{n}} , as in the theory of stochastic processes. For example, <span>to study Brownian motion, probability is defined on a space of functions. When it's convenient to work with a dominating measure, the Radon-Nikodym theorem is used to define a density as the Radon-Nikodym derivative of the probability distribution of intere




#probability-theory
measure-theoretic treatment also allows us to work on probabilities outside \(\mathbb {R} ^{n}\), as in the theory of stochastic processes .

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Probability theory - Wikipedia
{\displaystyle \mu _{F}\,} induced by F . {\displaystyle F\,.} Along with providing better understanding and unification of discrete and continuous probabilities, <span>measure-theoretic treatment also allows us to work on probabilities outside R n {\displaystyle \mathbb {R} ^{n}} , as in the theory of stochastic processes. For example, to study Brownian motion, probability is defined on a space of functions. When it's convenient to work with a dominating measure, the Radon-Nikodym theorem is used to def




#probability-theory
Weak convergence is also called convergence in distribution. Most common shorthand notation: \({\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X}\)

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Probability theory - Wikipedia
F {\displaystyle F\,} of X {\displaystyle X\,} , wherever F {\displaystyle F\,} is continuous. <span>Weak convergence is also called convergence in distribution. Most common shorthand notation: X n → D X {\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X} Convergence in probability The sequence of random variables X 1 , X




#probability-theory
The sequence of random variables \(X_{1},X_{2},\dots \,\) is said to converge towards the random variable \(X\,\) in probability if \(\lim _{n\rightarrow \infty }P\left(\left|X_{n}-X\right|\geq \varepsilon \right)=0\) for every ε > 0. Most common shorthand notation: \({\displaystyle \displaystyle X_{n}\,{\xrightarrow {P}}\,X}\)

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Probability theory - Wikipedia
D X {\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X} Convergence in probability <span>The sequence of random variables X 1 , X 2 , … {\displaystyle X_{1},X_{2},\dots \,} is said to converge towards the random variable X {\displaystyle X\,} in probability if lim n → ∞ P ( | X n − X | ≥ ε ) = 0 {\displaystyle \lim _{n\rightarrow \infty }P\left(\left|X_{n}-X\right|\geq \varepsilon \right)=0} for every ε > 0. Most common shorthand notation: X n → P X {\displaystyle \displaystyle X_{n}\,{\xrightarrow {P}}\,X} Strong convergence The sequence of random variables X 1 , X




#probability-theory
The sequence of random variables \(X_{1},X_{2},\dots \,\) is said to converge towards the random variable \(X\,\) strongly if \(P(\lim _{n\rightarrow \infty }X_{n}=X)=1\). Strong convergence is also known as almost sure convergence. Most common shorthand notation: \({\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X}\)

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Probability theory - Wikipedia
→ P X {\displaystyle \displaystyle X_{n}\,{\xrightarrow {P}}\,X} Strong convergence <span>The sequence of random variables X 1 , X 2 , … {\displaystyle X_{1},X_{2},\dots \,} is said to converge towards the random variable X {\displaystyle X\,} strongly if P ( lim n → ∞ X n = X ) = 1 {\displaystyle P(\lim _{n\rightarrow \infty }X_{n}=X)=1} . Strong convergence is also known as almost sure convergence. Most common shorthand notation: X n → a . s . X {\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X} As the names indicate, weak convergence is weaker than strong convergence. In fact, strong convergence implies convergence in probability, and convergence in probability implies w




Flashcard 2976242076940

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#probability-theory
Question
Let \((\Omega ,{\mathcal {F}},P)\) be a probability space and \((E,{\mathcal {E}})\) a measurable space. Then [...] is a measurable function \(X\colon \Omega \to E\)
Answer
an \((E,{\mathcal {E}})\)-valued random variable


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Let \((\Omega ,{\mathcal {F}},P)\) be a probability space and \((E,{\mathcal {E}})\) a measurable space. Then an \((E,{\mathcal {E}})\)-valued random variable is a measurable function \(X\colon \Omega \to E\), which means that, for every subset \(B\in {\mathcal {E}}\), its preimage \(X^{-1}(B)\in {\mathcal {F}}\) where \(X^{-1}(B)=\{\omega :X

Original toplevel document

Random variable - Wikipedia
fined over any sets that can be derived either directly from continuous intervals of numbers or by a finite or countably infinite number of unions and/or intersections of such intervals. [2] The measure-theoretic definition is as follows. <span>Let ( Ω , F , P ) {\displaystyle (\Omega ,{\mathcal {F}},P)} be a probability space and ( E , E ) {\displaystyle (E,{\mathcal {E}})} a measurable space. Then an ( E , E ) {\displaystyle (E,{\mathcal {E}})} -valued random variable is a measurable function X : Ω → E {\displaystyle X\colon \Omega \to E} , which means that, for every subset B ∈ E {\displaystyle B\in {\mathcal {E}}} , its preimage X − 1 ( B ) ∈ F {\displaystyle X^{-1}(B)\in {\mathcal {F}}} where X − 1 ( B ) = { ω : X ( ω ) ∈ B } {\displaystyle X^{-1}(B)=\{\omega :X(\omega )\in B\}} . [5] This definition enables us to measure any subset B ∈ E {\displaystyle B\in {\mathcal {E}}} in the target space by looking at its preimage, which by assumption is measurable. In more intuitive terms, a member of Ω {\displaystyle \Omega } is a possible outcome, a member of







Flashcard 2976244436236

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#probability-theory
Question
Let \((\Omega ,{\mathcal {F}},P)\) be a probability space and \((E,{\mathcal {E}})\) a measurable space. Then an \((E,{\mathcal {E}})\)-valued random variable is a [...]
Answer
measurable function \(X\colon \Omega \to E\)

Although real valued random variables are common, there can also be other kinds of variables


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Let \((\Omega ,{\mathcal {F}},P)\) be a probability space and \((E,{\mathcal {E}})\) a measurable space. Then an \((E,{\mathcal {E}})\)-valued random variable is a measurable function \(X\colon \Omega \to E\), which means that, for every subset \(B\in {\mathcal {E}}\), its preimage \(X^{-1}(B)\in {\mathcal {F}}\) where \(X^{-1}(B)=\{\omega :X(\omega )\in B\}\). [5] This definition enables u

Original toplevel document

Random variable - Wikipedia
fined over any sets that can be derived either directly from continuous intervals of numbers or by a finite or countably infinite number of unions and/or intersections of such intervals. [2] The measure-theoretic definition is as follows. <span>Let ( Ω , F , P ) {\displaystyle (\Omega ,{\mathcal {F}},P)} be a probability space and ( E , E ) {\displaystyle (E,{\mathcal {E}})} a measurable space. Then an ( E , E ) {\displaystyle (E,{\mathcal {E}})} -valued random variable is a measurable function X : Ω → E {\displaystyle X\colon \Omega \to E} , which means that, for every subset B ∈ E {\displaystyle B\in {\mathcal {E}}} , its preimage X − 1 ( B ) ∈ F {\displaystyle X^{-1}(B)\in {\mathcal {F}}} where X − 1 ( B ) = { ω : X ( ω ) ∈ B } {\displaystyle X^{-1}(B)=\{\omega :X(\omega )\in B\}} . [5] This definition enables us to measure any subset B ∈ E {\displaystyle B\in {\mathcal {E}}} in the target space by looking at its preimage, which by assumption is measurable. In more intuitive terms, a member of Ω {\displaystyle \Omega } is a possible outcome, a member of







Flashcard 2976246795532

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#probability-theory
Question
Let \((\Omega ,{\mathcal {F}},P)\) be a probability space and \((E,{\mathcal {E}})\) a measurable space. Then an \((E,{\mathcal {E}})\)-valued random variable means that [... description ...].
Answer
every subset \(B\in {\mathcal {E}}\) has a preimage \(X^{-1}(B)\in {\mathcal {F}}\)

Pretty much like any measurable function, just with a probability measure.
where
\(X^{-1}(B)=\{\omega :X(\omega )\in B\}\)


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dy>Let \((\Omega ,{\mathcal {F}},P)\) be a probability space and \((E,{\mathcal {E}})\) a measurable space. Then an \((E,{\mathcal {E}})\)-valued random variable is a measurable function \(X\colon \Omega \to E\), which means that, for every subset \(B\in {\mathcal {E}}\), its preimage \(X^{-1}(B)\in {\mathcal {F}}\) where \(X^{-1}(B)=\{\omega :X(\omega )\in B\}\). [5] This definition enables us to measure any subset \(B\in {\mathcal {E}}\) in the target space by looking at its preimage, which by assumption is measurable. <body><

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Random variable - Wikipedia
fined over any sets that can be derived either directly from continuous intervals of numbers or by a finite or countably infinite number of unions and/or intersections of such intervals. [2] The measure-theoretic definition is as follows. <span>Let ( Ω , F , P ) {\displaystyle (\Omega ,{\mathcal {F}},P)} be a probability space and ( E , E ) {\displaystyle (E,{\mathcal {E}})} a measurable space. Then an ( E , E ) {\displaystyle (E,{\mathcal {E}})} -valued random variable is a measurable function X : Ω → E {\displaystyle X\colon \Omega \to E} , which means that, for every subset B ∈ E {\displaystyle B\in {\mathcal {E}}} , its preimage X − 1 ( B ) ∈ F {\displaystyle X^{-1}(B)\in {\mathcal {F}}} where X − 1 ( B ) = { ω : X ( ω ) ∈ B } {\displaystyle X^{-1}(B)=\{\omega :X(\omega )\in B\}} . [5] This definition enables us to measure any subset B ∈ E {\displaystyle B\in {\mathcal {E}}} in the target space by looking at its preimage, which by assumption is measurable. In more intuitive terms, a member of Ω {\displaystyle \Omega } is a possible outcome, a member of







Flashcard 2976249154828

Tags
#probability-theory
Question
Weak convergence is also called [...]. Most common shorthand notation: \({\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X}\)
Answer
convergence in distribution

by distribution we means the CDF (cumulative distribution function)


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Weak convergence is also called convergence in distribution. Most common shorthand notation: \({\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X}\)

Original toplevel document

Probability theory - Wikipedia
F {\displaystyle F\,} of X {\displaystyle X\,} , wherever F {\displaystyle F\,} is continuous. <span>Weak convergence is also called convergence in distribution. Most common shorthand notation: X n → D X {\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X} Convergence in probability The sequence of random variables X 1 , X







Flashcard 2976251514124

Tags
#probability-theory
Question
convergence in probability means that [...]. Most common shorthand notation: \({\displaystyle \displaystyle X_{n}\,{\xrightarrow {P}}\,X}\)
Answer
\(\lim _{n\rightarrow \infty }P\left(\left|X_{n}-X\right|\geq \varepsilon \right)=0\) for every ε > 0


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The sequence of random variables \(X_{1},X_{2},\dots \,\) is said to converge towards the random variable \(X\,\) in probability if \(\lim _{n\rightarrow \infty }P\left(\left|X_{n}-X\right|\geq \varepsilon \right)=0\) for every ε > 0. Most common shorthand notation: \({\displaystyle \displaystyle X_{n}\,{\xrightarrow {P}}\,X}\)

Original toplevel document

Probability theory - Wikipedia
D X {\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathcal {D}}}\,X} Convergence in probability <span>The sequence of random variables X 1 , X 2 , … {\displaystyle X_{1},X_{2},\dots \,} is said to converge towards the random variable X {\displaystyle X\,} in probability if lim n → ∞ P ( | X n − X | ≥ ε ) = 0 {\displaystyle \lim _{n\rightarrow \infty }P\left(\left|X_{n}-X\right|\geq \varepsilon \right)=0} for every ε > 0. Most common shorthand notation: X n → P X {\displaystyle \displaystyle X_{n}\,{\xrightarrow {P}}\,X} Strong convergence The sequence of random variables X 1 , X







Flashcard 2976253873420

Tags
#probability-theory
Question
strong convergence means that [...]. Most common shorthand notation: \({\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X}\)
Answer
\(P(\lim _{n\rightarrow \infty }X_{n}=X)=1\)


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The sequence of random variables \(X_{1},X_{2},\dots \,\) is said to converge towards the random variable \(X\,\) strongly if \(P(\lim _{n\rightarrow \infty }X_{n}=X)=1\). Strong convergence is also known as almost sure convergence. Most common shorthand notation: \({\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X}\)

Original toplevel document

Probability theory - Wikipedia
→ P X {\displaystyle \displaystyle X_{n}\,{\xrightarrow {P}}\,X} Strong convergence <span>The sequence of random variables X 1 , X 2 , … {\displaystyle X_{1},X_{2},\dots \,} is said to converge towards the random variable X {\displaystyle X\,} strongly if P ( lim n → ∞ X n = X ) = 1 {\displaystyle P(\lim _{n\rightarrow \infty }X_{n}=X)=1} . Strong convergence is also known as almost sure convergence. Most common shorthand notation: X n → a . s . X {\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X} As the names indicate, weak convergence is weaker than strong convergence. In fact, strong convergence implies convergence in probability, and convergence in probability implies w







Flashcard 2976256232716

Tags
#probability-theory
Question
Strong convergence is also known as [...].
Answer
almost sure convergence


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tml>The sequence of random variables \(X_{1},X_{2},\dots \,\) is said to converge towards the random variable \(X\,\) strongly if \(P(\lim _{n\rightarrow \infty }X_{n}=X)=1\). Strong convergence is also known as almost sure convergence. Most common shorthand notation: \({\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X}\) <html>

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Probability theory - Wikipedia
→ P X {\displaystyle \displaystyle X_{n}\,{\xrightarrow {P}}\,X} Strong convergence <span>The sequence of random variables X 1 , X 2 , … {\displaystyle X_{1},X_{2},\dots \,} is said to converge towards the random variable X {\displaystyle X\,} strongly if P ( lim n → ∞ X n = X ) = 1 {\displaystyle P(\lim _{n\rightarrow \infty }X_{n}=X)=1} . Strong convergence is also known as almost sure convergence. Most common shorthand notation: X n → a . s . X {\displaystyle \displaystyle X_{n}\,{\xrightarrow {\mathrm {a.s.} }}\,X} As the names indicate, weak convergence is weaker than strong convergence. In fact, strong convergence implies convergence in probability, and convergence in probability implies w







Flashcard 2976258592012

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#probability-theory
Question
to study Brownian motion, probability is defined on [...].
Answer
a space of functions


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to study Brownian motion, probability is defined on a space of functions.

Original toplevel document

Probability theory - Wikipedia
rk on probabilities outside R n {\displaystyle \mathbb {R} ^{n}} , as in the theory of stochastic processes. For example, <span>to study Brownian motion, probability is defined on a space of functions. When it's convenient to work with a dominating measure, the Radon-Nikodym theorem is used to define a density as the Radon-Nikodym derivative of the probability distribution of intere







Flashcard 2976260164876

Tags
#probability-theory
Question
measure-theoretic treatment also allows us to work on [...], as in the theory of stochastic processes .
Answer
probabilities outside \(\mathbb {R} ^{n}\)


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measure-theoretic treatment also allows us to work on probabilities outside \(\mathbb {R} ^{n}\), as in the theory of stochastic processes .

Original toplevel document

Probability theory - Wikipedia
{\displaystyle \mu _{F}\,} induced by F . {\displaystyle F\,.} Along with providing better understanding and unification of discrete and continuous probabilities, <span>measure-theoretic treatment also allows us to work on probabilities outside R n {\displaystyle \mathbb {R} ^{n}} , as in the theory of stochastic processes. For example, to study Brownian motion, probability is defined on a space of functions. When it's convenient to work with a dominating measure, the Radon-Nikodym theorem is used to def







Flashcard 2976261737740

Tags
#probability-theory
Question
The raison d'être of the measure-theoretic treatment of probability is that it [...], and makes the difference a question of which measure is used.
Answer
unifies the discrete and the continuous cases


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The raison d'être of the measure-theoretic treatment of probability is that it unifies the discrete and the continuous cases, and makes the difference a question of which measure is used.

Original toplevel document

Probability theory - Wikipedia
R n {\displaystyle \mathbb {R} ^{n}} and other continuous sample spaces. Measure-theoretic probability theory[edit] <span>The raison d'être of the measure-theoretic treatment of probability is that it unifies the discrete and the continuous cases, and makes the difference a question of which measure is used. Furthermore, it covers distributions that are neither discrete nor continuous nor mixtures of the two. An example of such distributions could be a mix of discrete and continuous distr







#vim
To check, run vim --version and see if +clipboard exists.

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A Great Vim Cheat Sheet
You should now be able to press [space]w in normal mode to save a file. [space]p should paste from the system clipboard (outside of Vim). If you can’t paste, it’s probably because Vim was not built with the system clipboard option. <span>To check, run vim --version and see if +clipboard exists. If it says -clipboard , you will not be able to copy from outside of Vim. For Mac users, homebrew install Vim with the clipboard option. Install homebrew and then run brew install vim




Flashcard 2976273534220

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#vim
Question
To check system clipboard compatibility, run [...] and see if +clipboard exists.
Answer
vim --version


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To check, run vim --version and see if +clipboard exists.

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A Great Vim Cheat Sheet
You should now be able to press [space]w in normal mode to save a file. [space]p should paste from the system clipboard (outside of Vim). If you can’t paste, it’s probably because Vim was not built with the system clipboard option. <span>To check, run vim --version and see if +clipboard exists. If it says -clipboard , you will not be able to copy from outside of Vim. For Mac users, homebrew install Vim with the clipboard option. Install homebrew and then run brew install vim







#shell
echo $SHELL # displays the shell you're using

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Bash CheatSheet for UNIX Systems --&gt; UPDATED VERSION --&gt; https://github.com/LeCoupa/awesome-cheatsheets · GitHub
in the foreground or bg in the background DELETE # deletes one character backward !! # repeats the last command exit # logs out of current session # 1. Bash Basics. export # displays all environment variables <span>echo $SHELL # displays the shell you're using echo $BASH_VERSION # displays bash version bash # if you want to use bash (type exit to go back to your normal shell) whereis bash # finds out where bash is on




Flashcard 2976278777100

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Question
[...] # displays the shell you're using
Answer
echo $SHELL


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echo $SHELL # displays the shell you're using

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Bash CheatSheet for UNIX Systems --&gt; UPDATED VERSION --&gt; https://github.com/LeCoupa/awesome-cheatsheets · GitHub
in the foreground or bg in the background DELETE # deletes one character backward !! # repeats the last command exit # logs out of current session # 1. Bash Basics. export # displays all environment variables <span>echo $SHELL # displays the shell you're using echo $BASH_VERSION # displays bash version bash # if you want to use bash (type exit to go back to your normal shell) whereis bash # finds out where bash is on







#best-practice
Treat the data (and its format) as immutable.

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n—if you've got thoughts, please contribute or share them. Data is immutable Don't ever edit your raw data, especially not manually, and especially not in Excel. Don't overwrite your raw data. Don't save multiple versions of the raw data. <span>Treat the data (and its format) as immutable. The code you write should move the raw data through a pipeline to your final analysis. You shouldn't have to run all of the steps every time you want to make a new figure (see Analysis




#best-practice
Therefore, by default, the data folder is included in the .gitignore file.

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new figure (see Analysis is a DAG), but anyone should be able to reproduce the final products with only the code in src and the data in data/raw . Also, if data is immutable, it doesn't need source control in the same way that code does. <span>Therefore, by default, the data folder is included in the .gitignore file. If you have a small amount of data that rarely changes, you may want to include the data in the repository. Github currently warns if files are over 50MB and rejects files over 100MB.




#best-practice
When we use notebooks in our work, we often subdivide the notebooks folder.

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unication Notebook packages like the Jupyter notebook, Beaker notebook, Zeppelin, and other literate programming tools are very effective for exploratory data analysis. However, these tools can be less effective for reproducing an analysis. <span>When we use notebooks in our work, we often subdivide the notebooks folder. For example, notebooks/exploratory contains initial explorations, whereas notebooks/reports is more polished work that can be exported as html to the reports directory. Since no




#best-practice
Notebooks are for exploration and communication

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for storing/syncing large data include AWS S3 with a syncing tool (e.g., s3cmd ), Git Large File Storage, Git Annex, and dat. Currently by default, we ask for an S3 bucket and use AWS CLI to sync data in the data folder with the server. <span>Notebooks are for exploration and communication Notebook packages like the Jupyter notebook, Beaker notebook, Zeppelin, and other literate programming tools are very effective for exploratory data analysis. However, these tools can




#best-practice
Follow a naming convention that shows the owner and the order the analysis was done in.

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control (e.g., diffs of the json are often not human-readable and merging is near impossible), we recommended not collaborating directly with others on Jupyter notebooks. There are two steps we recommend for using notebooks effectively: <span>Follow a naming convention that shows the owner and the order the analysis was done in. We use the format --.ipynb (e.g., 0.3-bull-visualize-distributions.ipynb ). Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data pr




#best-practice
  1. Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at src/data/make_dataset.py and load data from data/interim. If it's useful utility code, refactor it to src.

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. There are two steps we recommend for using notebooks effectively: Follow a naming convention that shows the owner and the order the analysis was done in. We use the format --.ipynb (e.g., 0.3-bull-visualize-distributions.ipynb ). <span>Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at src/data/make_dataset.py and load data from data/interim . If it's useful utility code, refactor it to src . Now by default we turn the project into a Python package (see the setup.py file). You can import your code and use it in notebooks with a cell like the following: # OPTIONAL: Lo




#best-practice
Now by default we turn the project into a Python package (see the setup.py file). You can import your code and use it in notebooks with a cell like the following:

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rts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at src/data/make_dataset.py and load data from data/interim . If it's useful utility code, refactor it to src . <span>Now by default we turn the project into a Python package (see the setup.py file). You can import your code and use it in notebooks with a cell like the following: # OPTIONAL: Load the "autoreload" extension so that code can change %load_ext autoreload # OPTIONAL: always reload modules so that as you change code in src, it gets loaded




#best-practice
We prefer make for managing steps that depend on each other, especially the long-running ones.

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an analysis you have long-running steps that preprocess data or train models. If these steps have been run already (and you have stored the output somewhere like the data/interim directory), you don't want to wait to rerun them every time. <span>We prefer make for managing steps that depend on each other, especially the long-running ones. Make is a common tool on Unix-based platforms (and is available for Windows). Following the make documentation, Makefile conventions, and portability guide will help ensure your Makef




#best-practice
The first step in reproducing an analysis is always reproducing the computational environment it was run in.

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re other tools for managing DAGs that are written in Python instead of a DSL (e.g., Paver, Luigi, Airflow, Snakemake, Ruffus, or Joblib). Feel free to use these if they are more appropriate for your analysis. Build from the environment up <span>The first step in reproducing an analysis is always reproducing the computational environment it was run in. You need the same tools, the same libraries, and the same versions to make everything play nicely together. One effective approach to this is use virtualenv (we recommend virtualenvwr




#best-practice
By listing all of your requirements in the repository (we include a requirements.txt file) you can easily track the packages needed to recreate the analysis.

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vironment it was run in. You need the same tools, the same libraries, and the same versions to make everything play nicely together. One effective approach to this is use virtualenv (we recommend virtualenvwrapper for managing virtualenvs). <span>By listing all of your requirements in the repository (we include a requirements.txt file) you can easily track the packages needed to recreate the analysis. Here is a good workflow: Run mkvirtualenv when creating a new project pip install the packages that your analysis needs Run pip freeze > requirements.txt to pin the exact pack




#best-practice

Store your secrets and config variables in a special file

Create a .env file in the project root folder. Thanks to the .gitignore, this file should never get committed into the version control repository.

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p secrets and configuration out of version control You really don't want to leak your AWS secret key or Postgres username and password on Github. Enough said — see the Twelve Factor App principles on this point. Here's one way to do this: <span>Store your secrets and config variables in a special file Create a .env file in the project root folder. Thanks to the .gitignore , this file should never get committed into the version control repository. Here's an example: # example .env file DATABASE_URL=postgres://username:password@localhost:5432/dbname AWS_ACCESS_KEY=myaccesskey AWS_SECRET_ACCESS_KEY=mysecretkey OTHER_VARIABLE=some




but setting the default editor and then using

 git commit - e 

might be much more comfortable.

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bash - Add line break to 'git commit -m' from the command line - Stack Overflow
up vote 16 down vote I hope this isn't leading too far away from the posted question, <span>but setting the default editor and then using git commit -e might be much more comfortable. share|edit|flag edited Dec 22 '17 at 6:21




Flashcard 2976311020812

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#git
Question

use [...] to write commit messages with default editor.

Answer

 git commit - e 

after setting the default editor,


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but setting the default editor and then using git commit - e might be much more comfortable.

Original toplevel document

bash - Add line break to 'git commit -m' from the command line - Stack Overflow
up vote 16 down vote I hope this isn't leading too far away from the posted question, <span>but setting the default editor and then using git commit -e might be much more comfortable. share|edit|flag edited Dec 22 '17 at 6:21







#best-practice
Providing metadata is a fundamental requirement when publishing data on the Web because data publishers and data consumers may be unknown to each other.

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Data on the Web Best Practices
to tasks where metadata are used, for example, discovery and reuse. Best Practice 1: Provide metadata Provide metadata for both human users and computer applications. Why <span>Providing metadata is a fundamental requirement when publishing data on the Web because data publishers and data consumers may be unknown to each other. Then, it is essential to provide information that helps human users and computer applications to understand the data as well as other important aspects that describes a dataset or a dis




Flashcard 2976318360844

Tags
#best-practice
Question
[...] is a fundamental requirement when publishing data on the Web because data publishers and data consumers may be unknown to each other.
Answer
Providing metadata


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Providing metadata is a fundamental requirement when publishing data on the Web because data publishers and data consumers may be unknown to each other.

Original toplevel document

Data on the Web Best Practices
to tasks where metadata are used, for example, discovery and reuse. Best Practice 1: Provide metadata Provide metadata for both human users and computer applications. Why <span>Providing metadata is a fundamental requirement when publishing data on the Web because data publishers and data consumers may be unknown to each other. Then, it is essential to provide information that helps human users and computer applications to understand the data as well as other important aspects that describes a dataset or a dis







Flashcard 2976319933708

Tags
#best-practice
Question

Store your [...] in a special .env file in the project root folder, which is included in .gitignore

Answer
secret tokens and config variables


statusnot learnedmeasured difficulty37% [default]last interval [days]               
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Store your secrets and config variables in a special file Create a .env file in the project root folder. Thanks to the .gitignore , this file should never get committed into the version control repository.

Original toplevel document

Home - Cookiecutter Data Science
p secrets and configuration out of version control You really don't want to leak your AWS secret key or Postgres username and password on Github. Enough said — see the Twelve Factor App principles on this point. Here's one way to do this: <span>Store your secrets and config variables in a special file Create a .env file in the project root folder. Thanks to the .gitignore , this file should never get committed into the version control repository. Here's an example: # example .env file DATABASE_URL=postgres://username:password@localhost:5432/dbname AWS_ACCESS_KEY=myaccesskey AWS_SECRET_ACCESS_KEY=mysecretkey OTHER_VARIABLE=some







Flashcard 2976322293004

Tags
#best-practice
Question

Store your secret tokens and config variablesin a special .env file in the [...] folder, which is included in .gitignore

Answer
project root


statusnot learnedmeasured difficulty37% [default]last interval [days]               
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Parent (intermediate) annotation

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Store your secrets and config variables in a special file Create a .env file in the project root folder. Thanks to the .gitignore , this file should never get committed into the version control repository.

Original toplevel document

Home - Cookiecutter Data Science
p secrets and configuration out of version control You really don't want to leak your AWS secret key or Postgres username and password on Github. Enough said — see the Twelve Factor App principles on this point. Here's one way to do this: <span>Store your secrets and config variables in a special file Create a .env file in the project root folder. Thanks to the .gitignore , this file should never get committed into the version control repository. Here's an example: # example .env file DATABASE_URL=postgres://username:password@localhost:5432/dbname AWS_ACCESS_KEY=myaccesskey AWS_SECRET_ACCESS_KEY=mysecretkey OTHER_VARIABLE=some







Flashcard 2976324128012

Tags
#best-practice
Question
listing all dependences in a requirements.txt file so you can easily [...].
Answer
track the packages needed to recreate the analysis


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By listing all of your requirements in the repository (we include a requirements.txt file) you can easily track the packages needed to recreate the analysis.

Original toplevel document

Home - Cookiecutter Data Science
vironment it was run in. You need the same tools, the same libraries, and the same versions to make everything play nicely together. One effective approach to this is use virtualenv (we recommend virtualenvwrapper for managing virtualenvs). <span>By listing all of your requirements in the repository (we include a requirements.txt file) you can easily track the packages needed to recreate the analysis. Here is a good workflow: Run mkvirtualenv when creating a new project pip install the packages that your analysis needs Run pip freeze > requirements.txt to pin the exact pack







Flashcard 2976326487308

Tags
#best-practice
Question
The first step in reproducing an analysis is always reproducing [...].
Answer
the computational environment it was run in


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The first step in reproducing an analysis is always reproducing the computational environment it was run in.

Original toplevel document

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re other tools for managing DAGs that are written in Python instead of a DSL (e.g., Paver, Luigi, Airflow, Snakemake, Ruffus, or Joblib). Feel free to use these if they are more appropriate for your analysis. Build from the environment up <span>The first step in reproducing an analysis is always reproducing the computational environment it was run in. You need the same tools, the same libraries, and the same versions to make everything play nicely together. One effective approach to this is use virtualenv (we recommend virtualenvwr







Flashcard 2976328060172

Tags
#best-practice
Question
We prefer [...] for managing steps that depend on each other, especially the long-running ones.
Answer


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We prefer make for managing steps that depend on each other, especially the long-running ones.

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an analysis you have long-running steps that preprocess data or train models. If these steps have been run already (and you have stored the output somewhere like the data/interim directory), you don't want to wait to rerun them every time. <span>We prefer make for managing steps that depend on each other, especially the long-running ones. Make is a common tool on Unix-based platforms (and is available for Windows). Following the make documentation, Makefile conventions, and portability guide will help ensure your Makef







Flashcard 2976329633036

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#best-practice
Question
we turn the project into a Python package by adding the [...] file.
Answer
setup.py

So you can import your code and use it in notebooks and any other places


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Now by default we turn the project into a Python package (see the setup.py file). You can import your code and use it in notebooks with a cell like the following:

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rts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at src/data/make_dataset.py and load data from data/interim . If it's useful utility code, refactor it to src . <span>Now by default we turn the project into a Python package (see the setup.py file). You can import your code and use it in notebooks with a cell like the following: # OPTIONAL: Load the "autoreload" extension so that code can change %load_ext autoreload # OPTIONAL: always reload modules so that as you change code in src, it gets loaded







Flashcard 2976331992332

Tags
#best-practice
Question

[...] so codes can be reused in multiple notebooks.

Answer
refactor to src.


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Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at src/data/make_dataset.py and load data from data/interim . If it's useful utility code, refactor it to src . <span><body><html>

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. There are two steps we recommend for using notebooks effectively: Follow a naming convention that shows the owner and the order the analysis was done in. We use the format --.ipynb (e.g., 0.3-bull-visualize-distributions.ipynb ). <span>Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at src/data/make_dataset.py and load data from data/interim . If it's useful utility code, refactor it to src . Now by default we turn the project into a Python package (see the setup.py file). You can import your code and use it in notebooks with a cell like the following: # OPTIONAL: Lo







Flashcard 2976334351628

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#best-practice
Question
Follow a naming convention that shows at least [..., ..., and ...].
Answer
order, owner, and purpose

The other OOP


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Follow a naming convention that shows the owner and the order the analysis was done in.

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control (e.g., diffs of the json are often not human-readable and merging is near impossible), we recommended not collaborating directly with others on Jupyter notebooks. There are two steps we recommend for using notebooks effectively: <span>Follow a naming convention that shows the owner and the order the analysis was done in. We use the format --.ipynb (e.g., 0.3-bull-visualize-distributions.ipynb ). Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data pr







Flashcard 2976336710924

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#best-practice
Question
When we use notebooks in our work, we often [...].
Answer
subdivide the notebooks folder


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When we use notebooks in our work, we often subdivide the notebooks folder.

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unication Notebook packages like the Jupyter notebook, Beaker notebook, Zeppelin, and other literate programming tools are very effective for exploratory data analysis. However, these tools can be less effective for reproducing an analysis. <span>When we use notebooks in our work, we often subdivide the notebooks folder. For example, notebooks/exploratory contains initial explorations, whereas notebooks/reports is more polished work that can be exported as html to the reports directory. Since no







Flashcard 2976338283788

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Question
Notebooks are for [...]
Answer
exploration and communication


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Notebooks are for exploration and communication

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for storing/syncing large data include AWS S3 with a syncing tool (e.g., s3cmd ), Git Large File Storage, Git Annex, and dat. Currently by default, we ask for an S3 bucket and use AWS CLI to sync data in the data folder with the server. <span>Notebooks are for exploration and communication Notebook packages like the Jupyter notebook, Beaker notebook, Zeppelin, and other literate programming tools are very effective for exploratory data analysis. However, these tools can







Flashcard 2976339856652

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#best-practice
Question
Therefore, by default, the data folder is [...].
Answer
included in the .gitignore file

i.e. not in version control


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Therefore, by default, the data folder is included in the .gitignore file.

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new figure (see Analysis is a DAG), but anyone should be able to reproduce the final products with only the code in src and the data in data/raw . Also, if data is immutable, it doesn't need source control in the same way that code does. <span>Therefore, by default, the data folder is included in the .gitignore file. If you have a small amount of data that rarely changes, you may want to include the data in the repository. Github currently warns if files are over 50MB and rejects files over 100MB.







Flashcard 2976342215948

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Question
Treat the raw data (and its format) as [...].
Answer
immutable


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Treat the data (and its format) as immutable.

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n—if you've got thoughts, please contribute or share them. Data is immutable Don't ever edit your raw data, especially not manually, and especially not in Excel. Don't overwrite your raw data. Don't save multiple versions of the raw data. <span>Treat the data (and its format) as immutable. The code you write should move the raw data through a pipeline to your final analysis. You shouldn't have to run all of the steps every time you want to make a new figure (see Analysis







#Make
Besides building programs, Make can be used to manage any project where some files must be updated automatically from others whenever the others change.

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Make (software) - Wikipedia
y how to derive the target program. Though integrated development environments and language-specific compiler features can also be used to manage a build process, Make remains widely used, especially in Unix and Unix-like operating systems. <span>Besides building programs, Make can be used to manage any project where some files must be updated automatically from others whenever the others change. Contents [hide] 1 Origin 2 Derivatives 3 Behavior 4 Makefile 4.1 Rules 4.2 Macros 4.3 Suffix rules 4.4 Pattern rules 4.5 Other elements 5 Example makefiles 6 See also 7 R




#Make

Make is invoked with a list of target file names to build as command-line arguments:

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Make (software) - Wikipedia
urce) and the transformation actions might be to convert the file to some specific format, copy the result into a content management system, and then send e-mail to a predefined set of users indicating that the above actions were performed. <span>Make is invoked with a list of target file names to build as command-line arguments: make [TARGET ...] Without arguments, Make builds the first target that appears in its makefile, which is traditionally a symbolic "phony" target named all. Make d




#Make
Make decides whether a target needs to be regenerated by comparing file modification times.

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Make (software) - Wikipedia
a list of target file names to build as command-line arguments: make [TARGET ...] Without arguments, Make builds the first target that appears in its makefile, which is traditionally a symbolic "phony" target named all. <span>Make decides whether a target needs to be regenerated by comparing file modification times. [31] This solves the problem of avoiding the building of files which are already up to date, but it fails when a file changes but its modification time stays in the past. Such changes




#Make
This solves the problem of avoiding the building of files which are already up to date, but it fails when a file changes but its modification time stays in the past.

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Make (software) - Wikipedia
ut arguments, Make builds the first target that appears in its makefile, which is traditionally a symbolic "phony" target named all. Make decides whether a target needs to be regenerated by comparing file modification times. [31] <span>This solves the problem of avoiding the building of files which are already up to date, but it fails when a file changes but its modification time stays in the past. Such changes could be caused by restoring an older version of a source file, or when a network filesystem is a source of files and its clock or timezone is not synchronized with the mac




#Make
Make searches the current directory for the makefile to use

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Make (software) - Wikipedia
The user must handle this situation by forcing a complete build. Conversely, if a source file's modification time is in the future, it triggers unnecessary rebuilding, which may inconvenience users. Makefile[edit] Main article: Makefile <span>Make searches the current directory for the makefile to use, e.g. GNU make searches files in order for a file named one of GNUmakefile, makefile, Makefile and then runs the specified (or default) target(s) from (only) that file. The makefile




#Make
The makefile language is similar to declarative programming.[32][33][34][35] This class of language, in which necessary end conditions are described but the order in which actions are to be taken is not important

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Make (software) - Wikipedia
akefile Make searches the current directory for the makefile to use, e.g. GNU make searches files in order for a file named one of GNUmakefile, makefile, Makefile and then runs the specified (or default) target(s) from (only) that file. <span>The makefile language is similar to declarative programming. [32] [33] [34] [35] This class of language, in which necessary end conditions are described but the order in which actions are to be taken is not important, is sometimes confusing to programmers used to imperative programming. One problem in build automation is the tailoring of a build process to a given platform. For instance, the compi




Make allows us to specify what depends on what and how to update things that are out of date.

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Automation and Make: Introduction
Make’s fundamental concepts are common across build tools. GNU Make is a free, fast, well-documented, and very popular Make implementation. From now on, we will focus on it, and when we say Make, we mean GNU Make. Key Points <span>Make allows us to specify what depends on what and how to update things that are out of date. lesson home next episode Copyright © 2016–2