Example of Poisson-Gamma models in Bayesian inference in R programing.
We take an example of Poisson data (artificially generated) of size 20 and model the prior with a Gamma(2,2) distribution. We then sample from the posterior Gamma(19,22) and compute posterior quantities using direct sampling from the identified distribution.
We also show how to derive the Jeffreys prior, which is an improper prior but has the propriety of being weakly informative.
Then using we show an example of multi-stage Gibbs sampling for a Poisson process with Gamma prior.
Python implementation will be added soon.