Introduction to Gibbs sampling

Gibbs sampling is a Markov Chain Monte Carlo (MCMC) technique used for statistical inference and sampling from complex probability distributions, especially in Bayesian statistics. It was proposed by Josiah Willard Gibbs, a physicist and mathematician, in the late 19th century. The main goal of Gibbs sampling is to approximate the joint distribution of multiple variables by iteratively sampling from their conditional distributions while keeping the other variables fixed. This sampling process eventually converges to the desired joint distribution. Let's explain Gibbs sampling using a simple analogy…