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Probabilistic_Programming

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# Probabilistic Programming

A collection of examples to learn Probabilistic Programming

## Resources

- https://benlambertdotcom.files.wordpress.com/2019/03/bayesianbook_problemsanswers_including_errata.pdf
- http://www.stat.columbia.edu/~gelman/book/
    - https://github.com/avehtari/BDA_R_demos
    - [Datasets in BDA3](http://www.stat.columbia.edu/~gelman/book/data/)
    - https://avehtari.github.io/BDA_course_Aalto/
    
## RStan

- [Coin Flip Example](rstan/coin_flip_r/README.md)
- [8 Schools Example](rstan/school_example_r/README.md)
- [Rats Example](rstan/rats_r/README.md)
- [Titanic Kaggle with Stan](rstan/titanic_kaggle)

### References

- http://faculty.ucr.edu/~jflegal/203/STAN_tutorial.pdf
- [A Student’s Guide to Bayesian Statistics by Ben Lambert](https://github.com/alexandrahotti/Solutions-to-A-Students-Guide-to-Bayesian-Statistics-by-Ben-Lambert)

## PyStan

- [8 Schools Example PyStan](pystan/school_example_py/README.md)
- [Coin Flip Example](pystan/coin_flip_py/README.md)

## Julia Stan

- https://astrostatistics.psu.edu/su14/lectures/BayesComp2014LabMCMCv1.pdf
- https://mc-stan.org/users/interfaces/julia-stan
- http://stanjulia.github.io/Stan.jl/stable/INTRO.html

## PyMC3

### References

- https://github.com/pymc-devs/pymc3
- https://people.duke.edu/~ccc14/sta-663/PyMC3.html
- https://medium.com/airy-science/bayesian-inference-with-probabilistic-programming-using-pymc3-a00702ccd9e0

## Time Series

- https://www.unofficialgoogledatascience.com/2017/07/fitting-bayesian-structural-time-series.html?m=1
- https://multithreaded.stitchfix.com/blog/2016/04/21/forget-arima/

## Multi-Level Models

- https://www.rensvandeschoot.com/tutorials/brms-started/
- https://www.rensvandeschoot.com/tutorials/lme4/

## brms

- https://vuorre.netlify.app/post/2017/01/02/how-to-compare-two-groups-with-robust-bayesian-estimation-using-r-stan-and-brms/
- https://www.fionamseaton.com/tutorial/misc/brms-examples/

## rstanarm

- [Introduction to Bayesian Computation Using the rstanarm R Package](https://youtu.be/z7zOzL9Rrzs)

## Misc

- https://mathvault.ca/statistical-significance/
- https://betanalpha.github.io/assets/case_studies/principled_bayesian_workflow.html
- https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
- https://blog.floydhub.com/naive-bayes-for-machine-learning/
- https://ryxcommar.com/2019/09/06/some-things-you-maybe-didnt-know-about-linear-regression/
- https://towardsdatascience.com/an-introduction-to-bayesian-inference-in-pystan-c27078e58d53
- https://chi-feng.github.io/mcmc-demo/
- https://github.com/fonnesbeck/statistical-analysis-python-tutorial
- https://towardsdatascience.com/how-bayes-theorem-helped-win-the-second-world-war-7f3be5f4676c
- https://stackoverflow.com/questions/54853017/why-is-my-python-implementation-of-metropolis-algorithm-mcmc-so-slow
- https://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/
- https://www.evanmiller.org/statistical-formulas-for-programmers.html
- https://srcd.onlinelibrary.wiley.com/doi/full/10.1111/cdev.12169
- https://bookdown.org/content/3686/stan.html
- http://dm13450.github.io/2020/11/03/BayesPointProcess.html
- https://www.tweag.io/blog/2019-10-25-mcmc-intro1/
- https://towardsdatascience.com/importance-sampling-introduction-e76b2c32e744
- https://www.r-bloggers.com/2014/09/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/
- http://elevanth.org/blog/2017/11/28/build-a-better-markov-chain/
- https://github.com/chi-feng/mcmc-demo
- https://towardsdatascience.com/explaining-probability-plots-9e5c5d304703
- https://philippmuens.com/linear-and-multiple-regression-from-scratch/
- https://www.r-bloggers.com/2019/05/bayesian-modeling-using-stan-a-case-study/
- https://philippmuens.com/logistic-regression-from-scratch/
- https://github.com/asadoughi/stat-learning

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