###For those in a hurry
My first attempt to adapt to Python (using PyMC) the R code from "Doing Bayesian Data Analysis", by John K. Krushcke.
####Models done so far:
- Inferring two binomials proportions and their difference;
- Hierarchical prior for Bernoulli likelihood;
- Metric variable for a single group;
- Simple linear regression;
- Oneway ANOVA.
###Quick References
"Doing Bayesian Data Analysis", by John K. Krushcke
http://doingbayesiandataanalysis.blogspot.com.br/"Probabilistic Programming and Bayesian Methods for Hackers", by Cam Davidson-Pilon
https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/"PyMC", by Christopher Fonnesbeck, Anand Patil and David Huard
http://pymc-devs.github.io/pymc/
###For those with some time to spare
After many years avoiding any book on statistics, I found myself interested on bayesian methods when I first heard about them in the NLTK (Natural Language ToolKit for Python) Book. I read some introductory texts on Bayes' Theorem, but even tough the concepts were somewhat clear, I still wasn't able to understand how useful it was.
Then I found Krushcke's "Doing Bayesian Data Analysis". It was love at first few-pages-glance. Seriously, the book is great, specially for those who know nothing about statistics, like myself. The language is accessible, there are tons of examples, the hierarchical models are cleverly illustrated using a unique style... What are you still doing here? Go buy the book!
Ahem, where was I? Oh, yeah. Krushcke's book. Have I mentioned that it uses R and JAGS (or BUGS) to implement the concepts and models? It's really a great deal: you learn bayesian statistics, general probability AND R. But I had problems with R syntax. It's so clumsy, and weird and... Well, I don't like it. Great packages, but I really prefer Python.
Being a Python fan, I had to find a way to adapt the original code. It was made possible after I discovered PyMC in Cam Davidson-Pilon "Probabilistic Programming and Bayesian Methods for Hackers". Also a great book, published in an original media: IPython Notebooks - you read the book, you read (and edit!) the code and run it all in your favorite browser. Did I mention it is free?
So, as a exercise in bayesian statistics and in Python programming, I adapted some models used by Krushcke in his book to Python, using PyMC (and Numpy and Matplotlib). This is just a first attempt, and I have only "translated" Krushcke's model to Python - and in a very non-pythonic way. There's plenty of room for improvement, but the code shouldn't break if the data is entered correctly. The code is also heavily commented - after all, the point here is to exercise the concepts and programming skills.
Feel free to fork and modify and pull request - all suggestions are welcome!