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

by   CamDavidsonPilon Jupyter Notebook Version: Current License: MIT

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

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. Probabilistic-Programming-and-Bayesian-Methods-for-Hackers has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

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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### Support

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers has a medium active ecosystem.
It has 25641 star(s) with 7774 fork(s). There are 1383 watchers for this library.
It had no major release in the last 6 months.
There are 152 open issues and 88 have been closed. On average issues are closed in 211 days. There are 47 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of Probabilistic-Programming-and-Bayesian-Methods-for-Hackers is current.

### Quality

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers has 0 bugs and 0 code smells.

### Security

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.

Permissive licenses have the least restrictions, and you can use them in most projects.

### Reuse

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers releases are not available. You will need to build from source code and install.
Installation instructions are available. Examples and code snippets are not available.
It has 807 lines of code, 52 functions and 19 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.

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

No Key Features are available at this moment for Probabilistic-Programming-and-Bayesian-Methods-for-Hackers.

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

No Code Snippets are available at this moment for Probabilistic-Programming-and-Bayesian-Methods-for-Hackers.

## Community Discussions

QUESTION

Understanding the parameters of pymc3 package
Asked 2020-May-24 at 11:28

Answered 2020-May-24 at 11:28
1. `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`.

2. `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`.

3. `chains`: This parameter is used to say how many "chains" 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.

QUESTION

Tensorflow 2 -Probability: ValueError: Failed to convert a NumPy array to a Tensor (Unsupported numpy type: NPY_INT)
Asked 2020-May-08 at 21:36

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):

...

Answered 2020-May-08 at 21:36

problem 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.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

## Vulnerabilities

No vulnerabilities reported

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

If you would like to run the Jupyter notebooks locally, (option 1. above), you'll need to install the following:.
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.

## Support

The current chapter list is not finalized. If you see something that is missing (MCMC, MAP, Bayesian networks, good prior choices, Potential classes etc.), feel free to start there.Cleaning up Python code and making code more PyMC-esqueGiving better explanationsSpelling/grammar mistakesSuggestionsContributing to the Jupyter notebook stylesAll commits are welcome, even if they are minor ;)If you are unfamiliar with Github, you can email me contributions to the email below.

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