# on 12-May-2018 (Sat)

#### Flashcard 1637624712460

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In "Some Exploratory Discourse on Metadiscourse," Vande Kopple proposes two levels of writing. On one level, he says, the author provides propositional material concerning the subject matter in the text. On the other level, the author "does not add propositional material" but does "help our readers organize, classify, interpret, evaluate, and react to such material" (83). This is where metadiscourse operates. It follows that metadiscourse becomes "[...]" (83). 6

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es not add propositional material" but does "help our readers organize, classify, interpret, evaluate, and react to such material" (83). This is where metadiscourse operates. It follows that metadiscourse becomes "<span>discourse about discourse or communication about communication" (83). 6<span><body><html>

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#### Annotation 2962476371212

 #best-practice #software-engineering continuous integration (CI) is the practice of merging all developer working copies to a shared mainline several times a day

Continuous integration - Wikipedia
Standards and Bodies of Knowledge BABOK CMMI IEEE standards ISO 9001 ISO/IEC standards PMBOK SWEBOK Glossaries Artificial intelligence Computer science Electrical and electronics engineering v t e In software engineering, <span>continuous integration (CI) is the practice of merging all developer working copies to a shared mainline several times a day. [1] Grady Booch first named and proposed CI in his 1991 method, [2] although he did not advocate integrating several times a day. Extreme programming (XP) adopted the concept of CI a

#### Flashcard 2962477944076

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[...] is the practice of merging all developer working copies to a shared mainline several times a day
continuous integration (CI)

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continuous integration (CI) is the practice of merging all developer working copies to a shared mainline several times a day

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Continuous integration - Wikipedia
Standards and Bodies of Knowledge BABOK CMMI IEEE standards ISO 9001 ISO/IEC standards PMBOK SWEBOK Glossaries Artificial intelligence Computer science Electrical and electronics engineering v t e In software engineering, <span>continuous integration (CI) is the practice of merging all developer working copies to a shared mainline several times a day. [1] Grady Booch first named and proposed CI in his 1991 method, [2] although he did not advocate integrating several times a day. Extreme programming (XP) adopted the concept of CI a

#### Annotation 2962480041228

 #best-practice Travis CI is a hosted, distributed[2] continuous integration service used to build and test software projects hosted at GitHub

Travis CI - Wikipedia
ravis CI Developer(s) Travis CI community Repository https://github.com/travis-ci/travis-ci [imagelink] Written in Ruby Platform Web Type Continuous integration License MIT License [1] Website travis-ci.org (Free) travis-ci.com (Pro) <span>Travis CI is a hosted, distributed [2] continuous integration service used to build and test software projects hosted at GitHub. [3] Open source projects may be tested at no charge via travis-ci.org. Private projects may be tested at travis-ci.com on a fee basis. TravisPro provides custom deployments of a prop

#### Annotation 2962481614092

 #best-practice Travis CI is configured by adding a file named .travis.yml, which is a YAML format text file, to the root directory of the repository.[5] This file specifies the programming language used, the desired building and testing environment (including dependencies which must be installed before the software can be built and tested), and various other parameters.

Travis CI - Wikipedia
unlikely that casual users could successfully integrate it on their own platforms. [4] Contents [hide] 1 Configuration 2 Operation 3 Integration 4 Company 5 See also 6 References 7 External links Configuration[edit source] <span>Travis CI is configured by adding a file named .travis.yml, which is a YAML format text file, to the root directory of the repository. [5] This file specifies the programming language used, the desired building and testing environment (including dependencies which must be installed before the software can be built and tested), and various other parameters. Operation[edit source] When Travis CI has been activated for a given repository, GitHub will notify it whenever new commits are pushed to that repository or a pull request is submit

#### Flashcard 2962483186956

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Travis CI is a [...] service used to build and test software projects hosted at GitHub
hosted, distributed[2] continuous integration

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Travis CI is a hosted, distributed [2] continuous integration service used to build and test software projects hosted at GitHub

#### Original toplevel document

Travis CI - Wikipedia
ravis CI Developer(s) Travis CI community Repository https://github.com/travis-ci/travis-ci [imagelink] Written in Ruby Platform Web Type Continuous integration License MIT License [1] Website travis-ci.org (Free) travis-ci.com (Pro) <span>Travis CI is a hosted, distributed [2] continuous integration service used to build and test software projects hosted at GitHub. [3] Open source projects may be tested at no charge via travis-ci.org. Private projects may be tested at travis-ci.com on a fee basis. TravisPro provides custom deployments of a prop

#### Flashcard 2962484759820

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Travis CI is configured by adding a file named [...] to the root directory of the repository.

.travis.yml

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Travis CI is configured by adding a file named .travis.yml, which is a YAML format text file, to the root directory of the repository. [5] This file specifies the programming language used, the desired building and testing environment (includi

#### Original toplevel document

Travis CI - Wikipedia
unlikely that casual users could successfully integrate it on their own platforms. [4] Contents [hide] 1 Configuration 2 Operation 3 Integration 4 Company 5 See also 6 References 7 External links Configuration[edit source] <span>Travis CI is configured by adding a file named .travis.yml, which is a YAML format text file, to the root directory of the repository. [5] This file specifies the programming language used, the desired building and testing environment (including dependencies which must be installed before the software can be built and tested), and various other parameters. Operation[edit source] When Travis CI has been activated for a given repository, GitHub will notify it whenever new commits are pushed to that repository or a pull request is submit

#### Flashcard 2962487119116

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Travis CI is configured by adding a file named .travis.yml to [...] .

the root directory of the repository

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Travis CI is configured by adding a file named .travis.yml, which is a YAML format text file, to the root directory of the repository. [5] This file specifies the programming language used, the desired building and testing environment (includi

#### Original toplevel document

Travis CI - Wikipedia
unlikely that casual users could successfully integrate it on their own platforms. [4] Contents [hide] 1 Configuration 2 Operation 3 Integration 4 Company 5 See also 6 References 7 External links Configuration[edit source] <span>Travis CI is configured by adding a file named .travis.yml, which is a YAML format text file, to the root directory of the repository. [5] This file specifies the programming language used, the desired building and testing environment (including dependencies which must be installed before the software can be built and tested), and various other parameters. Operation[edit source] When Travis CI has been activated for a given repository, GitHub will notify it whenever new commits are pushed to that repository or a pull request is submit

#### Annotation 2962491575564

 #best-practice cookiecutters is a command-line utility that creates projects from project templates

GitHub - audreyr/cookiecutter: A command-line utility that creates projects from cookiecutters (project templates). E.g. Python package projects, jQuery plugin projects.
Issues 103 Pull requests 54 Projects 0 Wiki Insights <span>A command-line utility that creates projects from cookiecutters (project templates). E.g. Python package projects, jQuery plugin projects. 2,454

#### Flashcard 2962493934860

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cookiecutters is a command-line utility that [...]
creates projects from project templates

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cookiecutters is a command-line utility that creates projects from project templates

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GitHub - audreyr/cookiecutter: A command-line utility that creates projects from cookiecutters (project templates). E.g. Python package projects, jQuery plugin projects.
Issues 103 Pull requests 54 Projects 0 Wiki Insights <span>A command-line utility that creates projects from cookiecutters (project templates). E.g. Python package projects, jQuery plugin projects. 2,454

#### Annotation 2962498391308

 #best-practice To use ReciPy simply add the import line to the very top of your Python script:

GitHub - recipy/recipy: Effortless method to record provenance in Python
cipy required MongoDB to be installed and set up manually. This is no longer required, as a pure Python database (TinyDB) is used instead. Also, the GUI is now integrated fully into recipy and does not require installing separately. Usage <span>Simply add the following line to the top of your Python script: import recipy Note that this must be the very top line of your script, before you import anything else. Then just run your script as usual, and all of the data will be logged into the TinyDB database (don't worry, the database is automatically created if needed). You can then use the r

#### Annotation 2962499964172

 #best-practice To find out the details of a run which created a file test.npy you can recipy search test.npy

GitHub - recipy/recipy: Effortless method to record provenance in Python
standard script) Alternatively, run an unmodified script with python -m recipy SCRIPT [ARGS ...] to enable recipy logging. This invokes recipy's module entry point, which takes care of import recipy for you, before running your script. i<span>t will produce an output called test.npy . To find out the details of the run which created this file you can search using recipy search test.npy and it will display information like the following: Created by robin on 2015-05-25 19:00:15.631000 Ran /Users/robin/code/recipy/example_script.py using /usr/local/opt/python/bin/python2.7 Git: commit 91a245e5ea82f33ae58380629b6586883c

#### Annotation 2962501537036

 #best-practice With ReciPy to view a run with GUI just run recipy gui and a browser window will open with an interface that you can use to search all of your recipy 'runs':

GitHub - recipy/recipy: Effortless method to record provenance in Python
586883cca3ac4, in repo /Users/robin/code/recipy, with origin git@github.com:recipy/recipy.git Environment: Darwin-14.3.0-x86_64-i386-64bit, python 2.7.9 (default, Feb 10 2015, 03:28:08) Inputs: Outputs: /Users/robin/code/recipy/test.npy <span>An alternative way to view this is to use the GUI. Just run recipy gui and a browser window will open with an interface that you can use to search all of your recipy 'runs': [imagelink] If you want to log inputs and outputs of files read or written with built-in open, you need to do a little more work. Either use recipy.open (only requires import rec

#### Flashcard 2962504158476

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To use ReciPy simply add [...] to the very top of your Python script:

the import line

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To use ReciPy simply add the import line to the very top of your Python script:

#### Original toplevel document

GitHub - recipy/recipy: Effortless method to record provenance in Python
cipy required MongoDB to be installed and set up manually. This is no longer required, as a pure Python database (TinyDB) is used instead. Also, the GUI is now integrated fully into recipy and does not require installing separately. Usage <span>Simply add the following line to the top of your Python script: import recipy Note that this must be the very top line of your script, before you import anything else. Then just run your script as usual, and all of the data will be logged into the TinyDB database (don't worry, the database is automatically created if needed). You can then use the r

#### Flashcard 2962506517772

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To find out the details of a run which created a file test.npy you can [...]

recipy search test.npy

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To find out the details of a run which created a file test.npy you can recipy search test.npy

#### Original toplevel document

GitHub - recipy/recipy: Effortless method to record provenance in Python
standard script) Alternatively, run an unmodified script with python -m recipy SCRIPT [ARGS ...] to enable recipy logging. This invokes recipy's module entry point, which takes care of import recipy for you, before running your script. i<span>t will produce an output called test.npy . To find out the details of the run which created this file you can search using recipy search test.npy and it will display information like the following: Created by robin on 2015-05-25 19:00:15.631000 Ran /Users/robin/code/recipy/example_script.py using /usr/local/opt/python/bin/python2.7 Git: commit 91a245e5ea82f33ae58380629b6586883c

#### Flashcard 2962508877068

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With ReciPy to view a run with GUI just run [...] and a browser window will open with an interface that you can use to search all of your recipy 'runs':

recipy gui

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With ReciPy to view a run with GUI just run recipy gui and a browser window will open with an interface that you can use to search all of your recipy 'runs':

#### Original toplevel document

GitHub - recipy/recipy: Effortless method to record provenance in Python
586883cca3ac4, in repo /Users/robin/code/recipy, with origin git@github.com:recipy/recipy.git Environment: Darwin-14.3.0-x86_64-i386-64bit, python 2.7.9 (default, Feb 10 2015, 03:28:08) Inputs: Outputs: /Users/robin/code/recipy/test.npy <span>An alternative way to view this is to use the GUI. Just run recipy gui and a browser window will open with an interface that you can use to search all of your recipy 'runs': [imagelink] If you want to log inputs and outputs of files read or written with built-in open, you need to do a little more work. Either use recipy.open (only requires import rec

#### Annotation 2962513333516

 #best-practice nbdime provides tools for diffing and merging of Jupyter Notebooks.

GitHub - jupyter/nbdime: Tools for diffing and merging of Jupyter notebooks.
README.md Installation | Documentation | Contributing | Development Install | Testing | License | Getting help nbdime Jupyter Notebook Diff and Merge tools [imagelink] [imagelink] [imagelink] [imagelink] <span>nbdime provides tools for diffing and merging of Jupyter Notebooks. nbdiff compare notebooks in a terminal-friendly way nbmerge three-way merge of notebooks with automatic conflict resolution nbdiff-web shows you a rich rendered diff of notebooks n

#### Annotation 2962514906380

 #best-practice nbdiff compare notebooks in a terminal-friendly way

GitHub - jupyter/nbdime: Tools for diffing and merging of Jupyter notebooks.
ibuting | Development Install | Testing | License | Getting help nbdime Jupyter Notebook Diff and Merge tools [imagelink] [imagelink] [imagelink] [imagelink] nbdime provides tools for diffing and merging of Jupyter Notebooks. <span>nbdiff compare notebooks in a terminal-friendly way nbmerge three-way merge of notebooks with automatic conflict resolution nbdiff-web shows you a rich rendered diff of notebooks nbmerge-web gives you a web-based three-way merge tool

#### Annotation 2962516479244

 #best-practice nbmerge three-way merge of notebooks with automatic conflict resolution

GitHub - jupyter/nbdime: Tools for diffing and merging of Jupyter notebooks.
se | Getting help nbdime Jupyter Notebook Diff and Merge tools [imagelink] [imagelink] [imagelink] [imagelink] nbdime provides tools for diffing and merging of Jupyter Notebooks. nbdiff compare notebooks in a terminal-friendly way <span>nbmerge three-way merge of notebooks with automatic conflict resolution nbdiff-web shows you a rich rendered diff of notebooks nbmerge-web gives you a web-based three-way merge tool for notebooks nbshow present a single notebook in a terminal-friendly wa

#### Annotation 2962518052108

 #best-practice nbdiff-web shows you a rich rendered diff of notebooks

GitHub - jupyter/nbdime: Tools for diffing and merging of Jupyter notebooks.
gelink] [imagelink] [imagelink] [imagelink] nbdime provides tools for diffing and merging of Jupyter Notebooks. nbdiff compare notebooks in a terminal-friendly way nbmerge three-way merge of notebooks with automatic conflict resolution <span>nbdiff-web shows you a rich rendered diff of notebooks nbmerge-web gives you a web-based three-way merge tool for notebooks nbshow present a single notebook in a terminal-friendly way Diffing notebooks in the terminal: [imagelink] M

#### Annotation 2962519624972

 #best-practice nbmerge-web gives you a web-based three-way merge tool for notebooks

GitHub - jupyter/nbdime: Tools for diffing and merging of Jupyter notebooks.
ovides tools for diffing and merging of Jupyter Notebooks. nbdiff compare notebooks in a terminal-friendly way nbmerge three-way merge of notebooks with automatic conflict resolution nbdiff-web shows you a rich rendered diff of notebooks <span>nbmerge-web gives you a web-based three-way merge tool for notebooks nbshow present a single notebook in a terminal-friendly way Diffing notebooks in the terminal: [imagelink] Merging notebooks in a browser: [imagelink] Installation Install

#### Annotation 2962521197836

 #best-practice nbshow present a single notebook in a terminal-friendly way

GitHub - jupyter/nbdime: Tools for diffing and merging of Jupyter notebooks.
ompare notebooks in a terminal-friendly way nbmerge three-way merge of notebooks with automatic conflict resolution nbdiff-web shows you a rich rendered diff of notebooks nbmerge-web gives you a web-based three-way merge tool for notebooks <span>nbshow present a single notebook in a terminal-friendly way Diffing notebooks in the terminal: [imagelink] Merging notebooks in a browser: [imagelink] Installation Install nbdime with pip: pip install nbdime See the installation

#### Flashcard 2962524867852

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[...] provides tools for diffing and merging of Jupyter Notebooks.

nbdime

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nbdime provides tools for diffing and merging of Jupyter Notebooks.

#### Original toplevel document

GitHub - jupyter/nbdime: Tools for diffing and merging of Jupyter notebooks.
README.md Installation | Documentation | Contributing | Development Install | Testing | License | Getting help nbdime Jupyter Notebook Diff and Merge tools [imagelink] [imagelink] [imagelink] [imagelink] <span>nbdime provides tools for diffing and merging of Jupyter Notebooks. nbdiff compare notebooks in a terminal-friendly way nbmerge three-way merge of notebooks with automatic conflict resolution nbdiff-web shows you a rich rendered diff of notebooks n

#### Flashcard 2962526440716

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nbdime provides tools for [...].

diffing and merging of Jupyter Notebooks

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nbdime provides tools for diffing and merging of Jupyter Notebooks.

#### Original toplevel document

GitHub - jupyter/nbdime: Tools for diffing and merging of Jupyter notebooks.
README.md Installation | Documentation | Contributing | Development Install | Testing | License | Getting help nbdime Jupyter Notebook Diff and Merge tools [imagelink] [imagelink] [imagelink] [imagelink] <span>nbdime provides tools for diffing and merging of Jupyter Notebooks. nbdiff compare notebooks in a terminal-friendly way nbmerge three-way merge of notebooks with automatic conflict resolution nbdiff-web shows you a rich rendered diff of notebooks n

#### Annotation 2962530897164

 #best-practice nbval is a pytest plugin adds functionality to recognise and collect Jupyter notebooks. The intended purpose of nbval tests is to determine whether execution of the stored inputs match the stored outputs of the .ipynb file. Whilst also ensuring that the notebooks are running without errors.

GitHub - computationalmodelling/nbval: A py.test plugin to validate Jupyter notebooks
Fix missing coverage dependency 4 months ago README.md Py.test plugin for validating Jupyter notebooks [imagelink] [imagelink] [imagelink] <span>The plugin adds functionality to py.test to recognise and collect Jupyter notebooks. The intended purpose of the tests is to determine whether execution of the stored inputs match the stored outputs of the .ipynb file. Whilst also ensuring that the notebooks are running without errors. The tests were designed to ensure that Jupyter notebooks (especially those for reference and documentation), are executing consistently. Each cell is taken as a test, a cell that do

#### Flashcard 2962535877900

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nbval is a pytest plugin adds functionality to [...].

recognise and collect Jupyter notebooks

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nbval is a pytest plugin adds functionality to recognise and collect Jupyter notebooks. The intended purpose of nbval tests is to determine whether execution of the stored inputs match the stored outputs of the .ipynb file. Whilst also ensuring that the notebooks are

#### Original toplevel document

GitHub - computationalmodelling/nbval: A py.test plugin to validate Jupyter notebooks
Fix missing coverage dependency 4 months ago README.md Py.test plugin for validating Jupyter notebooks [imagelink] [imagelink] [imagelink] <span>The plugin adds functionality to py.test to recognise and collect Jupyter notebooks. The intended purpose of the tests is to determine whether execution of the stored inputs match the stored outputs of the .ipynb file. Whilst also ensuring that the notebooks are running without errors. The tests were designed to ensure that Jupyter notebooks (especially those for reference and documentation), are executing consistently. Each cell is taken as a test, a cell that do

#### Flashcard 2962538499340

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The intended purpose of nbval tests is to determine whether [.... matches ...].

execution of the stored inputs matches the stored outputs of the .ipynb file

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nbval is a pytest plugin adds functionality to recognise and collect Jupyter notebooks. The intended purpose of nbval tests is to determine whether execution of the stored inputs match the stored outputs of the .ipynb file. Whilst also ensuring that the notebooks are running without errors.

#### Original toplevel document

GitHub - computationalmodelling/nbval: A py.test plugin to validate Jupyter notebooks
Fix missing coverage dependency 4 months ago README.md Py.test plugin for validating Jupyter notebooks [imagelink] [imagelink] [imagelink] <span>The plugin adds functionality to py.test to recognise and collect Jupyter notebooks. The intended purpose of the tests is to determine whether execution of the stored inputs match the stored outputs of the .ipynb file. Whilst also ensuring that the notebooks are running without errors. The tests were designed to ensure that Jupyter notebooks (especially those for reference and documentation), are executing consistently. Each cell is taken as a test, a cell that do

#### Flashcard 2962540858636

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Apart from checking input-output match, nbval tests also ensures that [...].

the notebooks are running without errors

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test plugin adds functionality to recognise and collect Jupyter notebooks. The intended purpose of nbval tests is to determine whether execution of the stored inputs match the stored outputs of the .ipynb file. Whilst also ensuring that <span>the notebooks are running without errors. <span><body><html>

#### Original toplevel document

GitHub - computationalmodelling/nbval: A py.test plugin to validate Jupyter notebooks
Fix missing coverage dependency 4 months ago README.md Py.test plugin for validating Jupyter notebooks [imagelink] [imagelink] [imagelink] <span>The plugin adds functionality to py.test to recognise and collect Jupyter notebooks. The intended purpose of the tests is to determine whether execution of the stored inputs match the stored outputs of the .ipynb file. Whilst also ensuring that the notebooks are running without errors. The tests were designed to ensure that Jupyter notebooks (especially those for reference and documentation), are executing consistently. Each cell is taken as a test, a cell that do

#### Annotation 2962547936524

 #betancourt #probability-theory For example, the fundamental differences between discrete and continuous spaces arise from their different topologies. In particular, all of the common spaces, such as the integers and the real numbers, have unique topologies from which many of their more-intuitive properties arise.

Probability Theory (For Scientists and Engineers)
opposites – by definition the empty set and the full space are both topologically open and topologically closed or, shudder, topologically clopen. The topology of a space provides a formal way to define important properties like continuity. <span>For example, the fundamental differences between discrete and continuous spaces arise from their different topologies. In particular, all of the common spaces, such as the integers and the real numbers, have unique topologies from which many of their more-intuitive properties arise. Although superficially similar, a topology is distinct from a σσ-algebra. For example, the topologically open sets are not closed under the complement operation. If we combine all of

#### Annotation 2962550033676

 #betancourt #probability-theory Although superficially similar, a topology is distinct from a σ σ \sigma -algebra. For example, the topologically open sets are not closed under the complement operation. If we combine all of the topologically open sets and topologically closed sets defined by a topology, however, then we get a proper σ σ \sigma -algebra. σ σ \sigma -algebras constructed from the topology of a space are denoted Borel σ σ \sigma -algebras (Folland 1999) . Because common spaces have natural topologies they consequently also have natural Borel σ σ \sigma -algebras that exclude pathological sets that can cause problems.

#### Annotation 2962552130828

 #betancourt #probability-theory Once the philosophy has been stripped away, probability theory is simply the study of probability distributions, and the transformations of distributions between different spaces. a probability distribution assigns real values between 0 and 1 to suitable sets of measurable spaces,

Probability Theory (For Scientists and Engineers)
space are denoted Borel σσ-algebras (Folland 1999). Because common spaces have natural topologies they consequently also have natural Borel σσ-algebras that exclude pathological sets that can cause problems. 2 Mathematical Logistics <span>Once the philosophy has been stripped away, probability theory is simply the study of an object, a probability distribution that assigns values to sets, and the transformations of that object. To be fair, most of pure mathematics also ultimately reduces to studying objects and their transformations! Fortunately, the basic concepts of probability distributions and their manipu

#### Annotation 2962554490124

 #betancourt #probability-theory In particular, this countable additivity property allows us to cover complex neighborhoods, such as that enclosed by a smooth surface, with an infinite collection of sets and then calculate the probability allocated to that neighborhood.

Probability Theory (For Scientists and Engineers)
oss spaces with an infinite number of subsets, such as the real numbers, however, then we need to go a bit further and require self-consistency over any countable collection of disjoint sets, ℙπ[∪∞n=1An]=∑n=1∞ℙπ[An].Pπ[∪n=1∞An]=∑n=1∞Pπ[An]. <span>In particular, this countable additivity property allows us to cover complex neighborhoods, such as that enclosed by a smooth surface, with an infinite collection of sets and then calculate the probability allocated to that neighborhood. In addition to self-consistency we have to ensure that we assign all of the total probability in our allocation. This requires that all of the probability is allocated to the full spac

#### Annotation 2962556587276

 #betancourt #probability-theory A probability distribution is then completely specified by the probability triplet ( X ,  , π ) (X,X,π) (X, \mathcal{X}, \pi) which is often denoted more compactly as x ∼ π x∼π x \sim \pi where x ∈ X x∈X x \in X denotes the space, π π \pi denotes the probability distribution, and a valid σ σ \sigma -algebra is assumed.

#### Annotation 2962558684428

 #betancourt #probability-theory This weighting process emphasizes how the function behaves in neighborhoods of high probability while diminishing its behavior in neighborhoods of low probability.

Probability Theory (For Scientists and Engineers)
ummarize how functions of the form f:X→ℝf:X→R behave. Expectation values, 𝔼π[f]Eπ[f], reduce a function to a single real number by averaging the function output at every point, f(x)f(x), weighted by the probability assigned around that point. <span>This weighting process emphasizes how the function behaves in neighborhoods of high probability while diminishing its behavior in neighborhoods of low probability. How exactly, however, do we formally construct these expectation values? The only expectation values that we can immediately calculate in closed form are the expectations of an indica

#### Annotation 2962562354444

 #betancourt #probability-theory doubly measurable transformations

Probability Theory (For Scientists and Engineers)
nifestations of the same abstract system. The two manifestations, for example, might correspond to different choices of coordinate system, or different choices of units, or different choices of language capable of the same descriptions. These <span>doubly measurable transformations then serve as translations from one equivalent manifestation to another. Measurable transformations can also be used to project a probability distribution over a space onto a probabil

#### Annotation 2965643332876

 #viterbi-algorithm This algorithm generates a path $${\displaystyle X=(x_{1},x_{2},\ldots ,x_{T})}$$, which is a sequence of states $${\displaystyle x_{n}\in S=\{s_{1},s_{2},\dots ,s_{K}\}}$$ that generate the observations $${\displaystyle Y=(y_{1},y_{2},\ldots ,y_{T})}$$ with $${\displaystyle y_{n}\in O=\{o_{1},o_{2},\dots ,o_{N}\}}$$ ($$N$$ being the count of observations (observation space, see below)).

Viterbi algorithm - Wikipedia
lly needs to, and usually manages to get away with doing a lot less work (in software) than the ordinary Viterbi algorithm for the same result—however, it is not so easy [clarification needed] to parallelize in hardware. Pseudocode <span>This algorithm generates a path X = ( x 1 , x 2 , … , x T ) {\displaystyle X=(x_{1},x_{2},\ldots ,x_{T})} , which is a sequence of states x n ∈ S = { s 1 , s 2 , … , s K } {\displaystyle x_{n}\in S=\{s_{1},s_{2},\dots ,s_{K}\}} that generate the observations Y = ( y 1 , y 2 , … , y T ) {\displaystyle Y=(y_{1},y_{2},\ldots ,y_{T})} with y n ∈ O = { o 1 , o 2 , … , o N } {\displaystyle y_{n}\in O=\{o_{1},o_{2},\dots ,o_{N}\}} ( N {\displaystyle N} being the count of observations (observation space, see below)). Two 2-dimensional tables of size K × T {\displaystyle K\times T} are constructed: Each element

#### Flashcard 2965644905740

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This algorithm generates a path $${\displaystyle X=(x_{1},x_{2},\ldots ,x_{T})}$$, which is a sequence of states $${\displaystyle x_{n}\in S=\{s_{1},s_{2},\dots ,s_{K}\}}$$ that generate the observations $${\displaystyle Y=(y_{1},y_{2},\ldots ,y_{T})}$$ with $${\displaystyle y_{n}\in O=\{o_{1},[...],\dots ,o_{N}\}}$$ ($$N$$ being the count of observations (observation space, see below)).
x_{1},x_{2},\ldots ,x_{T})}\), which is a sequence of states $${\displaystyle x_{n}\in S=\{s_{1},s_{2},\dots ,s_{K}\}}$$ that generate the observations $${\displaystyle Y=(y_{1},y_{2},\ldots ,y_{T})}$$ with $${\displaystyle y_{n}\in O=\{o_{1},<span>o_{2},\dots ,o_{N}\}}$$ ($$N$$ being the count of observations (observation space, see below)). <span><body><html>