Bayesian Statistics

This repository contain Bayesians statistics implementation in R and Python. The directories available are:

Non-parametric Bayesian inference

• DPMM: contains the R code to implement Dirichlet Process Mixture Model (DPMM) using NIMBLE on the iris dataset. The primary referece to understand the theory behind DPMMs are the Bayesian non-parametric tutorials by Tamara Broderick.
• nature_of_priors: Examined the nature of a popular non-parametric bayesian prior called Dirichlet process. Primary reference for this file is the DP density estimation tutorial by PyMC.

Statistical software for Bayesian Statistics

• Nimble: Understanding NIMBLE syntax using a simple bayesian capture-recapture model example taken from the NIMBLE tutorial by Olivier Gimenez.
• STAN: Learning to perform posterior inference using RStan while following Chapters 6,7, and 8 of Bayes Rules! by Alicia A. Johnson, Miles Q. Ott, and Mine Dogucu.

Miscellaneous

• hare_lynx_dynamics: Performed likelihood estimation in R to estimate the parameters associated with the Lotka–Volterra system of differential equations.
• some_plots: Examined the nature of two popular priors used in Bayesian Statistics - beta and Dirichlet priors. Analyzed the effect of parameter choices on the nature of the resulting distributions.