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Flashcard 1729359645964

Tags
#Borges
Question
Lo que yo encuentro sobre todo malo en los deportes es la idea de que alguien [...] y de que alguien [...] , y de que este hecho suscite [...] .
Answer
gane, pierda, rivalidades


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Borges, Jorge Luis sur Twitter : "Lo que yo encuentro sobre todo malo en los deportes es la idea de que alguien gane y de que alguien pierda, y de que este hecho suscite riva… https://t.co/3bP06RPSjB"
Unblock @BorgesJorgeL Report Tweet Add to other Moment Add to new Moment <span>Lo que yo encuentro sobre todo malo en los deportes es la idea de que alguien gane y de que alguien pierda, y de que este hecho suscite rivalidades. Translate from Spanish Translated from Spanish by Bing What I find especially bad at sports is the idea that someone wins an







Flashcard 1767207210252

Tags
#incremental-reading
Question
[...] can be used to simplify wording of items
Answer
Context cues

The tags in my system


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Context cues can be used to simplify wording of items

Original toplevel document

20 rules of formulating knowledge in learning
This is roughly equivalent to 25-fold saving in time in the period of 20 years ! Such examples are not rare! They are most effectively handled with the all the preceding rules targeted on simplicity and against the interference <span>Context cues simplify wording You can use categories in SuperMemo 2000/2002, provide different branches of knowledge with a different look (different template), use reference labels (Title, Author, Date, etc.







Flashcard 1803172580620

Tags
#french #verbs
Question
In present tense, verbs whose infinitive ends in -ir add [...] to the stem.
Answer
-is, -is, -it, -issons, -issez, -issent

finir: je finis; tu finis; il, elle, on finit; nous finissons; vous finissez; ils, elles finissent


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Verbs whose infinitive ends in -ir add -is, -is, -it, -issons, -issez, -issent to the stem. finir : je finis; tu finis; il, elle, on finit; nous finissons; vous finissez; ils, elles finissent

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#jardin-dhiver
Je voudrais de la lumière
Comme en Nouvelle-Angleterre
Je veux changer d'atmosphère
Dans mon jardin d'hiver

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Henri Salvador – Jardin d'hiver Lyrics | Genius Lyrics
Jardin d'hiver Lyrics Je voudrais du soleil vert Des dentelles et des théières Des photos de bord de mer Dans mon jardin d'hiver <span>Je voudrais de la lumière Comme en Nouvelle-Angleterre Je veux changer d'atmosphère Dans mon jardin d'hiver Ta robe à fleur Sous la pluie de novembre Mes mains qui courent Je n'en peux plus de t'attendre Les années passent Qu'il est loin l'âge tendre Nul ne peut nous entendre Je voudrais du




#jardin-dhiver
Ta robe à fleur
Sous la pluie de novembre
Mes mains qui courent
Je n'en peux plus de t'attendre
Les années passent
Qu'il est loin l'âge tendre
Nul ne peut nous entendre

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Henri Salvador – Jardin d'hiver Lyrics | Genius Lyrics
Je voudrais du soleil vert Des dentelles et des théières Des photos de bord de mer Dans mon jardin d'hiver Je voudrais de la lumière Comme en Nouvelle-Angleterre Je veux changer d'atmosphère Dans mon jardin d'hiver <span>Ta robe à fleur Sous la pluie de novembre Mes mains qui courent Je n'en peux plus de t'attendre Les années passent Qu'il est loin l'âge tendre Nul ne peut nous entendre Je voudrais du Fred Astaire Revoir un Latécoère Je voudrais toujours te plaire Dans mon jardin d'hiver Je veux déjeuner par terre Comme au long des golfes clairs T'embrasser les yeux




#jardin-dhiver
Je voudrais du Fred Astaire
Revoir un Latécoère
Je voudrais toujours te plaire
Dans mon jardin d'hiver

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Henri Salvador – Jardin d'hiver Lyrics | Genius Lyrics
ouvelle-Angleterre Je veux changer d'atmosphère Dans mon jardin d'hiver Ta robe à fleur Sous la pluie de novembre Mes mains qui courent Je n'en peux plus de t'attendre Les années passent Qu'il est loin l'âge tendre Nul ne peut nous entendre <span>Je voudrais du Fred Astaire Revoir un Latécoère Je voudrais toujours te plaire Dans mon jardin d'hiver Je veux déjeuner par terre Comme au long des golfes clairs T'embrasser les yeux ouverts Dans mon jardin d'hiver Ta robe à fleur Sous la pluie de novembre Mes mains qui courent Je n'en




#jardin-dhiver
Je veux déjeuner par terre
Comme au long des golfes clairs
T'embrasser les yeux ouverts
Dans mon jardin d'hiver

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Henri Salvador – Jardin d'hiver Lyrics | Genius Lyrics
e novembre Mes mains qui courent Je n'en peux plus de t'attendre Les années passent Qu'il est loin l'âge tendre Nul ne peut nous entendre Je voudrais du Fred Astaire Revoir un Latécoère Je voudrais toujours te plaire Dans mon jardin d'hiver <span>Je veux déjeuner par terre Comme au long des golfes clairs T'embrasser les yeux ouverts Dans mon jardin d'hiver Ta robe à fleur Sous la pluie de novembre Mes mains qui courent Je n'en peux plus de t'attendre Les années passent Qu'il est loin l'âge tendre Nul ne peut nous entendre




Flashcard 2961672899852

Tags
#jardin-dhiver
Question
Je voudrais [...]
Comme en Nouvelle-Angleterre
Answer
de la lumière

(Je veux changer d'atmosphère
Dans mon jardin d'hiver)


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Je voudrais de la lumière Comme en Nouvelle-Angleterre Je veux changer d'atmosphère Dans mon jardin d'hiver

Original toplevel document

Henri Salvador – Jardin d'hiver Lyrics | Genius Lyrics
Jardin d'hiver Lyrics Je voudrais du soleil vert Des dentelles et des théières Des photos de bord de mer Dans mon jardin d'hiver <span>Je voudrais de la lumière Comme en Nouvelle-Angleterre Je veux changer d'atmosphère Dans mon jardin d'hiver Ta robe à fleur Sous la pluie de novembre Mes mains qui courent Je n'en peux plus de t'attendre Les années passent Qu'il est loin l'âge tendre Nul ne peut nous entendre Je voudrais du







Flashcard 2961675259148

Tags
#jardin-dhiver
Question
Je voudrais de la lumière
[...]
Answer
Comme en Nouvelle-Angleterre

(Je veux changer d'atmosphère
Dans mon jardin d'hiver)


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Je voudrais de la lumière Comme en Nouvelle-Angleterre Je veux changer d'atmosphère Dans mon jardin d'hiver

Original toplevel document

Henri Salvador – Jardin d'hiver Lyrics | Genius Lyrics
Jardin d'hiver Lyrics Je voudrais du soleil vert Des dentelles et des théières Des photos de bord de mer Dans mon jardin d'hiver <span>Je voudrais de la lumière Comme en Nouvelle-Angleterre Je veux changer d'atmosphère Dans mon jardin d'hiver Ta robe à fleur Sous la pluie de novembre Mes mains qui courent Je n'en peux plus de t'attendre Les années passent Qu'il est loin l'âge tendre Nul ne peut nous entendre Je voudrais du







Flashcard 2961678404876

Tags
#jardin-dhiver
Question
Je voudrais de la lumière
Comme en Nouvelle-Angleterre
[...]
Dans mon jardin d'hiver
Answer
Je veux changer d'atmosphère


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Je voudrais de la lumière Comme en Nouvelle-Angleterre Je veux changer d'atmosphère Dans mon jardin d'hiver

Original toplevel document

Henri Salvador – Jardin d'hiver Lyrics | Genius Lyrics
Jardin d'hiver Lyrics Je voudrais du soleil vert Des dentelles et des théières Des photos de bord de mer Dans mon jardin d'hiver <span>Je voudrais de la lumière Comme en Nouvelle-Angleterre Je veux changer d'atmosphère Dans mon jardin d'hiver Ta robe à fleur Sous la pluie de novembre Mes mains qui courent Je n'en peux plus de t'attendre Les années passent Qu'il est loin l'âge tendre Nul ne peut nous entendre Je voudrais du







Remember that using Bayes' Theorem doesn't make you a Bayesian. Quantifying uncertainty with probability makes you a Bayesian.

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\mathfrak{Michael Betancourt} sur Twitter : &quot;Remember that using Bayes' Theorem doesn't make you a Bayesian. Quantifying uncertainty with probability makes you a Bayesian.&quot;
Unblock @betanalpha Report Tweet Add to other Moment Add to new Moment <span>Remember that using Bayes' Theorem doesn't make you a Bayesian. Quantifying uncertainty with probability makes you a Bayesian. 10:20 PM - 5 Jan 2017 110 Retweets




Flashcard 2961687842060

Tags
#betancourt
Question
Remember that using Bayes' Theorem doesn't make you a Bayesian. [...] makes you a Bayesian.
Answer
Quantifying uncertainty with probability


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Remember that using Bayes' Theorem doesn't make you a Bayesian. Quantifying uncertainty with probability makes you a Bayesian.

Original toplevel document

\mathfrak{Michael Betancourt} sur Twitter : &quot;Remember that using Bayes' Theorem doesn't make you a Bayesian. Quantifying uncertainty with probability makes you a Bayesian.&quot;
Unblock @betanalpha Report Tweet Add to other Moment Add to new Moment <span>Remember that using Bayes' Theorem doesn't make you a Bayesian. Quantifying uncertainty with probability makes you a Bayesian. 10:20 PM - 5 Jan 2017 110 Retweets







#culture
It’s more helpful to think about exploitation and disrespect than to define cultural “ownership”

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The Question of Cultural Appropriation | Current Affairs
l Appropriation <span>It’s more helpful to think about exploitation and disrespect than to define cultural “ownership”… by Briahna Joy Gray The trouble with Elvis’s version of “Hound Dog” is not that i




Flashcard 2961694395660

Tags
#culture
Question
It’s more helpful to think about [...] than to define cultural “ownership”
Answer
exploitation and disrespect


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It’s more helpful to think about exploitation and disrespect than to define cultural “ownership”

Original toplevel document

The Question of Cultural Appropriation | Current Affairs
l Appropriation <span>It’s more helpful to think about exploitation and disrespect than to define cultural “ownership”… by Briahna Joy Gray The trouble with Elvis’s version of “Hound Dog” is not that i







Flashcard 2961695968524

Tags
#culture
Question
It’s more helpful to think about exploitation and disrespect than to define [...]
Answer
cultural “ownership”


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It’s more helpful to think about exploitation and disrespect than to define cultural “ownership”

Original toplevel document

The Question of Cultural Appropriation | Current Affairs
l Appropriation <span>It’s more helpful to think about exploitation and disrespect than to define cultural “ownership”… by Briahna Joy Gray The trouble with Elvis’s version of “Hound Dog” is not that i







#stan
Currently, the stan repo includes the language, the algorithms, and the service API for the interfaces.

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Home · stan-dev/stan Wiki · GitHub
math <- stan <- pystan <- rstan <- rstanarm <- cmdstan <- statastan <- matlabstan <- stan.jl <- MathematicaStan <span>Currently, the stan repo includes the language, the algorithms, and the service API for the interfaces. There are additional repos for tools such as the emacs mode, R plotting, R Shiny interface, web pages, etc. Developer Process Process Dev process overview Git process New Fu




Flashcard 2961701735692

Tags
#stan
Question
Currently, the stan repo includes [...].
Answer
the language, the algorithms, and the interfaces


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Currently, the stan repo includes the language, the algorithms, and the service API for the interfaces.

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Home · stan-dev/stan Wiki · GitHub
math <- stan <- pystan <- rstan <- rstanarm <- cmdstan <- statastan <- matlabstan <- stan.jl <- MathematicaStan <span>Currently, the stan repo includes the language, the algorithms, and the service API for the interfaces. There are additional repos for tools such as the emacs mode, R plotting, R Shiny interface, web pages, etc. Developer Process Process Dev process overview Git process New Fu







#best-practice #stan
Your model is only as good as your ability to communicate it to others, and communication is drastically improved by treating your Stan program as a piece of software and applying the best practices from the software development community.

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Stan Best Practices · stan-dev/stan Wiki · GitHub
tistical practice is a lifelong endeavor that continuously evolves as we learn more, but below are a list of best practices that will help accelerate you along that evolution as quickly as possible. 1. Treat your Stan program as software <span>Your model is only as good as your ability to communicate it to others, and communication is drastically improved by treating your Stan program as a piece of software and applying the best practices from the software development community. This helps not only to promote reproducibility but also to facilitate prompt answers from the Stan community when you run into problems. Write your program to be easily read. Use in




#best-practice #stan
Ideally, your analysis will be broken up into a Stan program, a data file, possibly an init file, and scripts that process the analysis in your favorite environment.

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Stan Best Practices · stan-dev/stan Wiki · GitHub
so that anyone can fit your model in any of the Stan interfaces. Saving data and inits is simplified with the stan_rdump and read_rdump functions provided by some of the interfaces -- see the individual interfaces for more information. <span>Ideally, your analysis will be broken up into a Stan program, a data file, possibly an init file, and scripts that process the analysis in your favorite environment. Because each of these files will constantly evolve as you improve your analysis, we highly recommend using version control software such as git and supporting services like GitHub.




#best-practice #stan
Data "corrections" such as background subtraction and deconvolution censor information in the data and limit how much you can learn in a given experiment

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Stan Best Practices · stan-dev/stan Wiki · GitHub
ng parameters all the way to the measurement and, ultimately, the raw data. Reason about how the data were generated with the experts who collected the data, breaking the process up into sequential steps that can be modeled piece by piece. <span>Data "corrections" such as background subtraction and deconvolution censor information in the data and limit how much you can learn in a given experiment. On the other hand, modeling the data generatively as a sum of signal plus background or signal smeared with measurement variability allows you to build an accurate model capable to ex




#best-practice #stan
modeling the data generatively as a sum of signal plus background or signal smeared with measurement variability allows you to build an accurate model capable to extracting as much information from the data as possible.

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Stan Best Practices · stan-dev/stan Wiki · GitHub
to sequential steps that can be modeled piece by piece. Data "corrections" such as background subtraction and deconvolution censor information in the data and limit how much you can learn in a given experiment. On the other hand, <span>modeling the data generatively as a sum of signal plus background or signal smeared with measurement variability allows you to build an accurate model capable to extracting as much information from the data as possible. 3. Start simple As important as generative modeling is, it's also important to start simple. Instead of trying to model every detail of the experiment immediately, begin with a si




#best-practice #stan
Instead of trying to model every detail of the experiment immediately, begin with a simple approximation that captures the gross features of the data generating process.

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Stan Best Practices · stan-dev/stan Wiki · GitHub
smeared with measurement variability allows you to build an accurate model capable to extracting as much information from the data as possible. 3. Start simple As important as generative modeling is, it's also important to start simple. <span>Instead of trying to model every detail of the experiment immediately, begin with a simple approximation that captures the gross features of the data generating process. Relative to long, complex models, simple models are much easier to communicate and drastically easier to debug. Once you can fit the initial simple model you can add more structure




#best-practice #stan
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.




#best-practice #stan
A nice consistency check is whether or not the 5% to 95% marginal posterior interval captures each of the "true" parameter values.

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Stan Best Practices · stan-dev/stan Wiki · GitHub
hat 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. <span>A nice consistency check is whether or not the 5% to 95% marginal posterior interval captures each of the "true" parameter values. If your fit does not pass this check then something is going wrong with the statistical algorithm and your model, and you will not be able to trust it when applied to real data. The




#best-practice #stan
The best generic diagnostic for the accuracy of a Markov chain Monte Carlo algorithm is the split R-hat statistic which quantifies the consistency of an ensemble of Markov chains.

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Stan Best Practices · stan-dev/stan Wiki · GitHub
mputing environment like R or Python. The simulation process itself will follow directly from the model, although if you have not focused on building your model generatively then it may not be easy to see that! Check generic diagnostics <span>The best generic diagnostic for the accuracy of a Markov chain Monte Carlo algorithm is the split R-hat statistic which quantifies the consistency of an ensemble of Markov chains. The idea is to run multiple Markov chains, 4 is an okay default but running more makes the diagnostic more sensitive, and ensure that they're all exploring the same regions of paramete




#best-practice #stan
Slow chains lead to low numbers of effective samples, and if there are too few effective samples then we can't accurate estimate the number of effective samples at all.

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Stan Best Practices · stan-dev/stan Wiki · GitHub
he others then all of the chains are suspect! In practice we have found that requiring Rhat < 1.1 is a good default requirement for each parameter. Another potential problem to keep in mind is Markov chains that explore very slowly. <span>Slow chains lead to low numbers of effective samples, and if there are too few effective samples then we can't accurate estimate the number of effective samples at all. A good check for such issues is the number of effective samples per iteration -- if N_eff / N < 0.001 then you should be suspect of the effective sample size calculation. Check




#best-practice #stan

The Folk Theorem of Statistical Computing is a heuristic developed by Andrew Gelman stating that most statistical computational problems are due not to the algorithm being used but rather the model itself.

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Stan Best Practices · stan-dev/stan Wiki · GitHub
in some interfaces, namely RStan, but they can be directly access in the fit output in all interfaces. There is an additional energy diagnostic in development that will be made available to the interfaces soon. 5. Heed the Folk Theorem <span>The Folk Theorem of Statistical Computing is a heuristic developed by Andrew Gelman stating that most statistical computational problems are due not to the algorithm being used but rather the model itself. For example, a poorly-identified model with diffuse priors will require sojourns into the extremes of parameter space that will break even the best statistical algorithms. Instead of




#best-practice #stan
The Folk Theorem is an important perspective because if forces the user to confront and evaluate the assumptions inherent to their model.

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Stan Best Practices · stan-dev/stan Wiki · GitHub
pace that will break even the best statistical algorithms. Instead of trying to build an algorithm that can handle such extreme values, we can simply replace the diffuse priors with weakly-informative priors and use the existing algorithm. <span>The Folk Theorem is an important perspective because if forces the user to confront and evaluate the assumptions inherent to their model. This is especially useful when adhering to a sequential model building process where problematic model structures are easy to identify. Moreover, the Folk Theorem provides continued m




Flashcard 2961724017932

Tags
#best-practice #stan
Question
[...] limit how much you can learn in a given experiment
Answer
Data "corrections"


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Data "corrections" such as background subtraction and deconvolution censor information in the data and limit how much you can learn in a given experiment

Original toplevel document

Stan Best Practices · stan-dev/stan Wiki · GitHub
ng parameters all the way to the measurement and, ultimately, the raw data. Reason about how the data were generated with the experts who collected the data, breaking the process up into sequential steps that can be modeled piece by piece. <span>Data "corrections" such as background subtraction and deconvolution censor information in the data and limit how much you can learn in a given experiment. On the other hand, modeling the data generatively as a sum of signal plus background or signal smeared with measurement variability allows you to build an accurate model capable to ex







Flashcard 2961726377228

Tags
#best-practice #stan
Question
The Folk Theorem forces the user to [...] inherent to their model.
Answer
confront and evaluate the assumptions


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The Folk Theorem is an important perspective because if forces the user to confront and evaluate the assumptions inherent to their model.

Original toplevel document

Stan Best Practices · stan-dev/stan Wiki · GitHub
pace that will break even the best statistical algorithms. Instead of trying to build an algorithm that can handle such extreme values, we can simply replace the diffuse priors with weakly-informative priors and use the existing algorithm. <span>The Folk Theorem is an important perspective because if forces the user to confront and evaluate the assumptions inherent to their model. This is especially useful when adhering to a sequential model building process where problematic model structures are easy to identify. Moreover, the Folk Theorem provides continued m







Flashcard 2961728736524

Tags
#best-practice #stan
Question

The Folk Theorem of Statistical Computing states that most statistical computational problems are due not to the algorithm being used but rather [...].

Answer
the model itself


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ml> The Folk Theorem of Statistical Computing is a heuristic developed by Andrew Gelman stating that most statistical computational problems are due not to the algorithm being used but rather the model itself. <html>

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Stan Best Practices · stan-dev/stan Wiki · GitHub
in some interfaces, namely RStan, but they can be directly access in the fit output in all interfaces. There is an additional energy diagnostic in development that will be made available to the interfaces soon. 5. Heed the Folk Theorem <span>The Folk Theorem of Statistical Computing is a heuristic developed by Andrew Gelman stating that most statistical computational problems are due not to the algorithm being used but rather the model itself. For example, a poorly-identified model with diffuse priors will require sojourns into the extremes of parameter space that will break even the best statistical algorithms. Instead of







Flashcard 2961731095820

Tags
#best-practice #stan
Question

[...] states that most statistical computational problems are due not to the algorithm being used but rather the model itself.

Answer
The Folk Theorem


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The Folk Theorem of Statistical Computing is a heuristic developed by Andrew Gelman stating that most statistical computational problems are due not to the algorithm being used but rather the model itse

Original toplevel document

Stan Best Practices · stan-dev/stan Wiki · GitHub
in some interfaces, namely RStan, but they can be directly access in the fit output in all interfaces. There is an additional energy diagnostic in development that will be made available to the interfaces soon. 5. Heed the Folk Theorem <span>The Folk Theorem of Statistical Computing is a heuristic developed by Andrew Gelman stating that most statistical computational problems are due not to the algorithm being used but rather the model itself. For example, a poorly-identified model with diffuse priors will require sojourns into the extremes of parameter space that will break even the best statistical algorithms. Instead of







Flashcard 2961733455116

Tags
#best-practice #stan
Question
Slow chains lead to low numbers of [...]
Answer
effective samples


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Slow chains lead to low numbers of effective samples, and if there are too few effective samples then we can't accurate estimate the number of effective samples at all.

Original toplevel document

Stan Best Practices · stan-dev/stan Wiki · GitHub
he others then all of the chains are suspect! In practice we have found that requiring Rhat < 1.1 is a good default requirement for each parameter. Another potential problem to keep in mind is Markov chains that explore very slowly. <span>Slow chains lead to low numbers of effective samples, and if there are too few effective samples then we can't accurate estimate the number of effective samples at all. A good check for such issues is the number of effective samples per iteration -- if N_eff / N < 0.001 then you should be suspect of the effective sample size calculation. Check







Flashcard 2961735814412

Tags
#best-practice #stan
Question
[...] indicates that the algorithm is not exploring the posterior efficiently
Answer
Slow chains


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Slow chains lead to low numbers of effective samples, and if there are too few effective samples then we can't accurate estimate the number of effective samples at all.

Original toplevel document

Stan Best Practices · stan-dev/stan Wiki · GitHub
he others then all of the chains are suspect! In practice we have found that requiring Rhat < 1.1 is a good default requirement for each parameter. Another potential problem to keep in mind is Markov chains that explore very slowly. <span>Slow chains lead to low numbers of effective samples, and if there are too few effective samples then we can't accurate estimate the number of effective samples at all. A good check for such issues is the number of effective samples per iteration -- if N_eff / N < 0.001 then you should be suspect of the effective sample size calculation. Check







Flashcard 2961738173708

Tags
#best-practice #stan
Question
The best generic diagnostic for the accuracy of a Markov chain Monte Carlo algorithm is [...]
Answer
the split R-hat statistic


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The best generic diagnostic for the accuracy of a Markov chain Monte Carlo algorithm is the split R-hat statistic which quantifies the consistency of an ensemble of Markov chains.

Original toplevel document

Stan Best Practices · stan-dev/stan Wiki · GitHub
mputing environment like R or Python. The simulation process itself will follow directly from the model, although if you have not focused on building your model generatively then it may not be easy to see that! Check generic diagnostics <span>The best generic diagnostic for the accuracy of a Markov chain Monte Carlo algorithm is the split R-hat statistic which quantifies the consistency of an ensemble of Markov chains. The idea is to run multiple Markov chains, 4 is an okay default but running more makes the diagnostic more sensitive, and ensure that they're all exploring the same regions of paramete







Flashcard 2961740533004

Tags
#best-practice #stan
Question
the split R-hat statistic quantifies the [...].
Answer
consistency of an ensemble of Markov chains


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The best generic diagnostic for the accuracy of a Markov chain Monte Carlo algorithm is the split R-hat statistic which quantifies the consistency of an ensemble of Markov chains.

Original toplevel document

Stan Best Practices · stan-dev/stan Wiki · GitHub
mputing environment like R or Python. The simulation process itself will follow directly from the model, although if you have not focused on building your model generatively then it may not be easy to see that! Check generic diagnostics <span>The best generic diagnostic for the accuracy of a Markov chain Monte Carlo algorithm is the split R-hat statistic which quantifies the consistency of an ensemble of Markov chains. The idea is to run multiple Markov chains, 4 is an okay default but running more makes the diagnostic more sensitive, and ensure that they're all exploring the same regions of paramete







Flashcard 2961742892300

Tags
#best-practice #stan
Question
A nice consistency check is whether or not [...] captures each of the "true" parameter values.
Answer
the 5% to 95% marginal posterior interval


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A nice consistency check is whether or not the 5% to 95% marginal posterior interval captures each of the "true" parameter values.

Original toplevel document

Stan Best Practices · stan-dev/stan Wiki · GitHub
hat 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. <span>A nice consistency check is whether or not the 5% to 95% marginal posterior interval captures each of the "true" parameter values. If your fit does not pass this check then something is going wrong with the statistical algorithm and your model, and you will not be able to trust it when applied to real data. The







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.

Original toplevel document

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 2961746038028

Tags
#best-practice #stan
Question
Instead of trying to model every detail of the experiment immediately, begin with a simple approximation that captures [...].
Answer
the gross features of the data generating process


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Instead of trying to model every detail of the experiment immediately, begin with a simple approximation that captures the gross features of the data generating process.

Original toplevel document

Stan Best Practices · stan-dev/stan Wiki · GitHub
smeared with measurement variability allows you to build an accurate model capable to extracting as much information from the data as possible. 3. Start simple As important as generative modeling is, it's also important to start simple. <span>Instead of trying to model every detail of the experiment immediately, begin with a simple approximation that captures the gross features of the data generating process. Relative to long, complex models, simple models are much easier to communicate and drastically easier to debug. Once you can fit the initial simple model you can add more structure







Flashcard 2961747610892

Tags
#best-practice #stan
Question
modeling the data generatively as [...] allows you to build an accurate model capable to extracting as much information from the data as possible.
Answer
a sum of signal plus background

or signal smeared with measurement variability


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modeling the data generatively as a sum of signal plus background or signal smeared with measurement variability allows you to build an accurate model capable to extracting as much information from the data as possible.

Original toplevel document

Stan Best Practices · stan-dev/stan Wiki · GitHub
to sequential steps that can be modeled piece by piece. Data "corrections" such as background subtraction and deconvolution censor information in the data and limit how much you can learn in a given experiment. On the other hand, <span>modeling the data generatively as a sum of signal plus background or signal smeared with measurement variability allows you to build an accurate model capable to extracting as much information from the data as possible. 3. Start simple As important as generative modeling is, it's also important to start simple. Instead of trying to model every detail of the experiment immediately, begin with a si







Flashcard 2961749970188

Tags
#best-practice #stan
Question
Ideally, your analysis will be broken up into a [...4 parts...].
Answer
Stan program, a data file, possibly an init file, and scripts that process the analysis in your favorite environment


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Ideally, your analysis will be broken up into a Stan program, a data file, possibly an init file, and scripts that process the analysis in your favorite environment.

Original toplevel document

Stan Best Practices · stan-dev/stan Wiki · GitHub
so that anyone can fit your model in any of the Stan interfaces. Saving data and inits is simplified with the stan_rdump and read_rdump functions provided by some of the interfaces -- see the individual interfaces for more information. <span>Ideally, your analysis will be broken up into a Stan program, a data file, possibly an init file, and scripts that process the analysis in your favorite environment. Because each of these files will constantly evolve as you improve your analysis, we highly recommend using version control software such as git and supporting services like GitHub.







#priors
Some principles we don't like: invariance, Jeffreys, entropy

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Prior Choice Recommendations · stan-dev/stan Wiki · GitHub
r closed forms or for Gibbs; we don't care about that Computational goal in Stan: reducing instability which can typically arise from bad geometry in the posterior, heavy tails that can cause Stan to adapt poorly and have heavy tails <span>Some principles we don't like: invariance, Jeffreys, entropy Weakly informative prior should contain enough information to regularize: the idea is that the prior rules out unreasonable parameter values but is not so strong as to rule out val




Flashcard 2961756523788

Tags
#priors
Question
Some principles we don't like: [...]
Answer
invariance, Jeffreys, entropy


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Some principles we don't like: invariance, Jeffreys, entropy

Original toplevel document

Prior Choice Recommendations · stan-dev/stan Wiki · GitHub
r closed forms or for Gibbs; we don't care about that Computational goal in Stan: reducing instability which can typically arise from bad geometry in the posterior, heavy tails that can cause Stan to adapt poorly and have heavy tails <span>Some principles we don't like: invariance, Jeffreys, entropy Weakly informative prior should contain enough information to regularize: the idea is that the prior rules out unreasonable parameter values but is not so strong as to rule out val







Flashcard 2961766747404

Tags
#Réflexe #myotatique
Question
Qu'est ce que le réflexe myotatique ou réflexe d'étirment ?
Answer
C'est la contraction d'un muscle déclenché par son étirement.


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Flashcard 2961768320268

Tags
#Réflexe #myotatique
Question
Donner deux exemples de réflexes myotatiques
Qu'est ce que cela provoque ?
Answer
Par exemple la percussion du tendon du muscles du quadriceps (réflexe rotulien) ou du triceps (réflexe achilléen).
Provoquent un étirement bref de ces muscles.


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Flashcard 2961770155276

Tags
#Réflexe #myotatique
Question
Comment est considéré cet étirement ?
Answer
Il est considéré comme un stimulus qui; par voie réflexe, déclenche la contraction de ce même muscle.


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Flashcard 2961771990284

Tags
#Réflexe #myotatique
Question
Que se passe-t-il lors d'un réflexe par percussion du tendon ?
Answer
la stimulation très brève déclenche une contraction, très brève elle aussi du muscle.


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Flashcard 2961773563148

Tags
#Réflexe #myotatique
Question
Que cherche-t-on à faire pendant un examen médical de routine ?

Que'est ce que cela apport au médecin ?
Answer
On cherhce l'aspect phasique du réflexe myotique (phasique, car entraînent un mouvement bref).

Il le renseigne sur le fonctionnement, état du système nerveux.


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#has-images

如何自己购买印度“救命药”(已上图)附加福利


spinit();

[金沙赌场] 注册即送27元,可提款,澳门博彩第一品牌!

国际品牌,值得信任.10元存取款,真人百家乐,龙虎斗,牛牛,AV捕鱼王,上万款老虎机游戏。持有澳门牌照、政府监督、大额无忧,百万提款3分钟火速到账,天天返水3.0%无上限!全网独家实时返水无需申请!
www.7739.com

[太阳城集团]注册即送300元,可提款,澳门博彩第一品牌!

国际品牌,值得信任.1元存取款,真人百家乐,轮盘,牛牛,AV捕鱼王,上万款老虎机游戏。持有澳门牌照、政府监督、大额无忧,百万提款3分钟火速到账,天天返水3.5%无上限!全网独家实时返水无需申请!
www.8827.com


右下角点击一下就可以直接和客服对话了
可能中国市场很大他们也会用翻译软件跟你中文交流 所以基本是没有障碍的
英文好的可以直接打电话过去联系
另外阿波罗官网药房是可以还价的,很奇葩的一个国家。
很多人不知道什么救命药,
比如治愈丙肝的吉利德,癌症肿瘤进口靶向药,这些在印度都是买到的。
价格悬殊可以说想多大就有多大

新人1024回复很慢 有需要了解更多的可以私信我留下VX
不知道留站外网址有没有违规
如有违规请版主及时删贴
另外给一波福利,印度的西药伟哥之前很多大神都介绍过
告诉大家几款印度的草本伟哥,不含西药成分的。
这几个亲试有效的,其实我们老祖宗以前应该也有很多的,要不古代那些皇帝怎么雨露均沾
真的不知道那些配方都遗落到哪里去了



  • null

游客多年 终得一码 非常感谢
没啥经验,就分享一些自己的作为印度代购的一些经验
之前看过百舸大神介绍的印度伟哥类很详细,就不多做介绍了
一个合格的印度代购主要是做什么,当然是做药了。
印度的药为什么便宜
1、政府保护仿制,充当“推销员”
1970年印度总理英迪拉·甘地主导修订了的《专利法》规定,对食品、药品只授予工艺专利,不授予产品专利,这意味着印度放弃了对药品化合物的知识产权保护,
制度上的宽松使得本国企业能够获得大量仿制药生产许可,从而为印度仿制药提供了快速扩张的空间。

2、人力成本低,技术雄厚
印度制药可谓占尽天时地利人和,制造业成本低、操作技术简单、劳动强度大,使得印度各个种类的制造业发展迅速,也逐渐积攒了雄厚的技术。
目前印度境内拥有FDA认证的药厂共有119家,可向美国出口约900种获得FDA批准的药物和制药原料;拥有英国药品管理局认证的药厂也已达80多家。

我给大家介绍的是如何购买印度这些救命药,可能这个方法会断了一些人的财路,但也希望更多人能吃得起吧
很多人可能也知道印度药,也找代购买过,有些医院的主治医生也都在推销,不得不说这是一个很大的利益链吧
其实这些要完全可以自己购买,价格上面肯定会优惠一些,毕竟只要倒手就要加价,不赚钱别人怎么生活,无可厚非吧
可恨的是那些骗子代购,骗这个钱真的是禽兽不如,对于需要这些药的家庭来说真的是雪上加霜

首先如何购买
印度大的药房有两家,阿波罗和罗氏,他们都是有网上药房的
以阿波罗举例:在阿波罗药房买药最大的难点不是语言问题,只要你会用电脑会上网,开个翻译软件和他们聊好了,他们不会不耐烦。虽然翻译软件翻译的可能不太准确,
但是大概的意思不会错的,彼此之间都能明白别人的意思,或者身边有英文水平高的朋友也可以请他帮忙。最大的难点就在信用卡上,必须要有VISA卡,
还要到银行去开通国际支付功能,还要有最少1万的额度。

进去阿波罗药房首页后点击右下角的“online”那里和工作人员沟通,告诉他你要什么和数量,跟他谈好价钱,然后他就会要求你提供医生开的处方药,一般我们的处方药是中文的,
只要用英文翻译好名字和药名和数量就可以了,提供处方药给他后(这个淘宝可以找某宝人工翻译),3个小时内他就会给你一个下单链接,付款之后他就会给你发货了,新手不会上图。
等学会的给大家上图演示一下

有几点需要注意下
1.收货人请填写病人姓名,手机号无所谓,因为一旦被扣可以凭处方,诊断书去海关拿药。只有极个别城市是没有人情的,大多数省份都是可以拿出来的,但请一次不要购买太多,有倒卖嫌疑
2,广西,福建,四川,江苏,山东(据说有个重要的会议在六月份),这几个地方是经常扣货的,广西,四川几乎见一个扣一个,
但都可以拿到,如果资料齐全的话,最好的找一个其他地区的亲戚,朋友,同学代收,转给你。
至于网址大家可以自行百度,另外如果请代购,请大家一定要担保交易,现在假的太多,不要相信什么违禁品不能担保交易,不管是国家还是某宝,都是处于一种模糊的态度,只是很多人不了解罢了
第一次发帖,胡乱写写,有什么问题可以私信我。我也是代购印度产品的,但不做这些“救命药”,做什么你们猜吧

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#PATH

When you type any command at the prompt (say, python), the system has a well-defined sequence of places that it looks for the executable. This sequence is defined in a system variable called PATH, which the user can specify. To see your PATH, you can type echo $PATH.

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Running Jupyter with multiple Python and IPython paths - Stack Overflow
thon installs and finds packages How Jupyter knows what Python to use For the sake of completeness, I'll try to do a quick ELI5 on each of these, so you'll know how to solve this issue in the best way for you. 1. Unix/Linux/OSX $PATH <span>When you type any command at the prompt (say, python ), the system has a well-defined sequence of places that it looks for the executable. This sequence is defined in a system variable called PATH , which the user can specify. To see your PATH , you can type echo $PATH . The result is a list of directories on your computer, which will be searched in order for the desired executable. From your output above, I assume that it contains this: $ echo




#PATH
The result is a list of directories on your computer, which will be searched in order for the desired executable.

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Running Jupyter with multiple Python and IPython paths - Stack Overflow
mpt (say, python ), the system has a well-defined sequence of places that it looks for the executable. This sequence is defined in a system variable called PATH , which the user can specify. To see your PATH , you can type echo $PATH . <span>The result is a list of directories on your computer, which will be searched in order for the desired executable. From your output above, I assume that it contains this: $ echo $PATH /usr/bin/:/Library/Frameworks/Python.framework/Versions/3.5/bin/:/usr/local/bin/ In windows echo %path% P




#PATH
When you run python and do something like import matplotlib, Python has to play a similar game to find the package you have in mind. Similar to $PATH in unix, Python has sys.path that specifies these

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Running Jupyter with multiple Python and IPython paths - Stack Overflow
many installations of the same program on your system. Changing the path is not too complicated; see e.g. How to permanently set $PATH on Linux?. Windows - How to set environment variables in Windows 10 2. How Python finds packages <span>When you run python and do something like import matplotlib , Python has to play a similar game to find the package you have in mind. Similar to $PATH in unix, Python has sys.path that specifies these: $ python >>> import sys >>> sys.path ['', '/Users/jakevdp/anaconda/lib/python3.5', '/Users/jakevdp/anaconda/lib/python3.5/site-packages', ...] Some importan




#PATH
by default, the first entry in sys.path is the current directory.

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Running Jupyter with multiple Python and IPython paths - Stack Overflow
nix, Python has sys.path that specifies these: $ python >>> import sys >>> sys.path ['', '/Users/jakevdp/anaconda/lib/python3.5', '/Users/jakevdp/anaconda/lib/python3.5/site-packages', ...] Some important things: <span>by default, the first entry in sys.path is the current directory. Also, unless you modify this (which you shouldn't do unless you know exactly what you're doing) you'll usually find something called site-packages in the path: this is the default pla




#PATH
is the current directory. Also, unless you modify this (which you shouldn't do unless you know exactly what you're doing) you'll usually find something called site-packages in the path: this is the default place that Python puts packages when you install them using python setup.py install, or pip, or conda, or a similar means.

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Running Jupyter with multiple Python and IPython paths - Stack Overflow
these: $ python >>> import sys >>> sys.path ['', '/Users/jakevdp/anaconda/lib/python3.5', '/Users/jakevdp/anaconda/lib/python3.5/site-packages', ...] Some important things: by default, the first entry in sys.path <span>is the current directory. Also, unless you modify this (which you shouldn't do unless you know exactly what you're doing) you'll usually find something called site-packages in the path: this is the default place that Python puts packages when you install them using python setup.py install , or pip , or conda , or a similar means. The important thing to note is that each python installation has its own site-packages , where packages are installed for that specific Python version . In other words, if you inst




#PATH
some Python packages come bundled with stand-alone scripts that you can run from the command line (examples are pip, ipython, jupyter, pep8, etc.) By default, these executables will be put in the same directory path as the Python used to install them, and are designed to work only with that Python installation.

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Running Jupyter with multiple Python and IPython paths - Stack Overflow
cause it was installed on a different Python! This is why in our twitter exchange I recommended you focus on one Python installation, and fix your $PATH so that you're only using the one you want to use. There's another component to this: <span>some Python packages come bundled with stand-alone scripts that you can run from the command line (examples are pip , ipython , jupyter , pep8 , etc.) By default, these executables will be put in the same directory path as the Python used to install them, and are designed to work only with that Python installation . That means that, as your system is set-up, when you run python , you get /usr/bin/python , but when you run ipython , you get /Library/Frameworks/Python.framework/Versions/3.5/bi




#PATH
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




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But in order for this to happen, Jupyter needs to know where to look for the associated executable: that is, it needs to know which path the python sits in.

These paths are specified in jupyter's kernelspec, and it's possible for the user to adjust them to their desires.

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Running Jupyter with multiple Python and IPython paths - Stack Overflow
for a non-existent Python version. Jupyter is set-up to be able to use a wide range of "kernels", or execution engines for the code. These can be Python 2, Python 3, R, Julia, Ruby... there are dozens of possible kernels to use. <span>But in order for this to happen, Jupyter needs to know where to look for the associated executable: that is, it needs to know which path the python sits in. These paths are specified in jupyter's kernelspec , and it's possible for the user to adjust them to their desires. For example, here's the list of kernels that I have on my system: $ jupyter kernelspec list Available kernels: python2.7 /Users/jakevdp/.ipython/kernels/python2.7 python3.




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here's the list of kernels that I have on my system:

 $ jupyter kernelspec list Available kernels : python2 . 7 / Users / jakevdp /. ipython / kernels / python2 . 7 python3 . 3 / Users / jakevdp /. ipython / kernels / python3 . 3 python3 . 4 / Users / jakevdp /. ipython / kernels / python3 . 4 python3 . 5 / Users / jakevdp /. ipython / kernels / python3 . 5 python2 / Users / jakevdp / Library / Jupyter / kernels / python2 python3 / Users / jakevdp / Library / Jupyter / kernels / python3 

Each of these is a directory containing some metadata that specifies the kernel name, the path to the executable, and other relevant info.

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Running Jupyter with multiple Python and IPython paths - Stack Overflow
know where to look for the associated executable: that is, it needs to know which path the python sits in. These paths are specified in jupyter's kernelspec , and it's possible for the user to adjust them to their desires. For example, <span>here's the list of kernels that I have on my system: $ jupyter kernelspec list Available kernels: python2.7 /Users/jakevdp/.ipython/kernels/python2.7 python3.3 /Users/jakevdp/.ipython/kernels/python3.3 python3.4 /Users/jakevdp/.ipython/kernels/python3.4 python3.5 /Users/jakevdp/.ipython/kernels/python3.5 python2 /Users/jakevdp/Library/Jupyter/kernels/python2 python3 /Users/jakevdp/Library/Jupyter/kernels/python3 Each of these is a directory containing some metadata that specifies the kernel name, the path to the executable, and other relevant info. You can adjust kernels manually, editing the metadata inside the directories listed above. The command to install a kernel can change depending on the kernel. IPython relies on the i




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IPython relies on the ipykernel package which contains a command to install a python kernel: for example

 $ python - m ipykernel install 

It will create a kernelspec associated with the Python executable you use to run this command. You can then choose this kernel in the Jupyter notebook to run your code with that Python.

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Running Jupyter with multiple Python and IPython paths - Stack Overflow
specifies the kernel name, the path to the executable, and other relevant info. You can adjust kernels manually, editing the metadata inside the directories listed above. The command to install a kernel can change depending on the kernel. <span>IPython relies on the ipykernel package which contains a command to install a python kernel: for example $ python -m ipykernel install It will create a kernelspec associated with the Python executable you use to run this command. You can then choose this kernel in the Jupyter notebook to run your code with that Python. You can see other options that ipykernel provides using the help command: $ python -m ipykernel install --help usage: ipython-kernel-install [-h] [--user] [--name NAME]