# Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | Bayesian Methods for Hackers `` : An introduction | Machine Learning library

## kandi X-RAY | Probabilistic-Programming-and-Bayesian-Methods-for-Hackers Summary

## kandi X-RAY | Probabilistic-Programming-and-Bayesian-Methods-for-Hackers Summary

the bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. the typical text on bayesian inference involves two to three chapters on probability theory, then enters what bayesian inference is. unfortunately, due to mathematical intractability of most bayesian models, the reader is only shown simple, artificial examples. this can leave the user with a so-what feeling about bayesian inference. in fact, this was the author's own prior opinion. after some recent success of bayesian methods in machine-learning competitions, i decided to investigate the subject again. even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand the methods. there was simply not enough literature bridging theory to practice. the problem with my misunderstanding was the disconnect between bayesian mathematics and probabilistic programming. that being said, i suffered then so the reader would not have to now. this book attempts to bridge the gap. if bayesian inference is the destination, then mathematical analysis is a particular path towards it. on the other hand, computing power is cheap enough that we can afford to take an alternate route via probabilistic

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## Probabilistic-Programming-and-Bayesian-Methods-for-Hackers Key Features

## Probabilistic-Programming-and-Bayesian-Methods-for-Hackers Examples and Code Snippets

## Community Discussions

Trending Discussions on Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

QUESTION

In python's `pymc3`

package, a typical model building works as follows (imported from https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb -

ANSWER

Answered 2020-May-24 at 11:28`tune`

: Markov Chain Monte Carlo samplers are based on the concept of Markov Chains. Markov Chains start from a random distribution and slowly converge to the distribution of your model (called stationary distribution). So, if you want to sample "real" (unbiased) samples from your model, you will need to "tune" (let it converge) the chain. So, by setting`tune=1000`

, you are saying pymc3 to let the chain converge to the distribution of your model for 1000 iteration. Once 1000 iterations are complete, start drawing from the distribution. This takes us to our next parameter`draws`

.`draws`

: This parameter says pymc3 how many samples you want to draw from your model's distribution (markov chain) once the tuning step is complete. So, by setting`draws=1000`

, you are saying pymc3 to draw 1000 samples. Now, sometimes, the markov chain doesn't converge and your get biased samples. How to test if your chain has converged or not? This takes us to our last parameter`chains`

.`chains`

: This parameter is used to say how many "" we want to sample. i. e: the number of markov chains to run. You can run more than one markov chain to see if the chain converged to its stationary distribution (which is your model's distribution) and if not how much divergent is it?? This is useful as, if one of the chain didn't converge, you can use alternate chains that you sampled. It is normally recommended to keep this parameter greater than 1 otherwise it makes it impossible to run some convergence checks.*chains*

QUESTION

This is a mysterious error --to me-- that keeps propping up.

For a reproducible example, you can find the Jupyter Notebook here: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter5_LossFunctions/Ch5_LossFunctions_TFP.ipynb) -- Chapter 5 (Loss Functions).

Conveniently, in this example, the data are artificial and constructed on the fly.

The part of the code that creates the problem is the following (I am running tensorflow 2):

...ANSWER

Answered 2020-May-08 at 21:36problem seems to come from

kernel = tfp.mcmc.SimpleStepSizeAdaptation( inner_kernel=kernel, num_adaptation_steps=int(burnin * 0.8))

in another similar example, I got same error. if you skip this line, it works.

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## Vulnerabilities

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## Install Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

Jupyter is a requirement to view the ipynb files. It can be downloaded here

Necessary packages are PyMC, NumPy, SciPy and Matplotlib. For Linux/OSX users, you should not have a problem installing the above, except for Matplotlib on OSX. For Windows users, check out pre-compiled versions if you have difficulty. also recommended, for data-mining exercises, are PRAW and requests.

New to Python or Jupyter, and help with the namespaces? Check out this answer.

In the styles/ directory are a number of files that are customized for the notebook. These are not only designed for the book, but they offer many improvements over the default settings of matplotlib and the Jupyter notebook. The in notebook style has not been finalized yet.

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