Visual Materials to Teach Gibbs Sampler
Yukari Shirota, Takako Hashimoto, Basabi Chakraborty · International Journal of Knowledge Engineering · 2016
Bayesian model of inference is widely used in various application fields such as data engineering or text processing.Using Bayes' theorem, we can obtain the posterior distribution function.When we conduct sampling using Markov chain Monte Carlo (MCMC), the most prominent MCMC algorithms are the Metropolis-Hastings and the Gibbs sampler, the latter being particularly useful in Bayesian analysis.This paper presents the visual teaching material for studying Gibbs sampler algorithm.Interaction with this material is supposed to enable students to deeply understand the mathematical process behind Gibbs sampling and encourages them to comprehend the mathematical expressions in the textbooks.