The posterior probability distribution is the heart of Bayesian statistics and a fundamental tool for Bayesian parameter estimation. Naturally, how to infer and build these distributions is a widely examined topic, the scope of which cannot fit in one blog. In this blog, we examine bayesian sampling using three basic, but fundamental techniques, importance sampling, Metropolis-Hastings sampling, and Gibbs sampling.
Starting in GAUSS version 12, a new suite of high quality and high-performance random number generators was introduced. While new projects should always use one of the modern RNG’s, it is sometimes necessary to exactly reproduce some work from the past. GAUSS has retained a set of older LCG’s, which will allow you to reproduce the random numbers from older GAUSS versions for many distributions.