This repository contains notebooks and code demonstrating key concepts in Bayesian statistics, especially concerning Bayesian computation. Much of the mterial is based off the book Bayesian Data Analysis by Gelman, et. al. (2004)
List and briefly describe each notebook included in the repository. Provide a summary of the content covered in each notebook and any relevant information about the datasets or analyses performed.
- sampling.ipynb: Some basic sampling methods useful for Bayesian computation: rejection sampling, importance sampling, and inverse CDF sampling.
- markovchain.ipynb: Basic implementation of Gibbs sampling and the Metropolis-Hastings algorithm.
A. Gelman, J. Carlin, H. Stern, and D. Rubin. Bayesian Data Analysis. Chapman and Hall/CRC, 2nd ed. edition, (2004)