Gibbs sampling is a ๐ŸŽฏ Markov Chain Monte Carlo technique for approximately sampling from a distribution . If our joint probability is hard to sample but the conditionals are computable, our transition samples one variable conditioned on all others.

Block Gibbs Sampling

Some are structured with many conditional independences; one example is the intra-layer nodes in a ๐Ÿšซ Restricted Boltzmann Machine. If this is the case, we can update many variables at once during our transition, a process known as Block Gibbs sampling.

Note

In latent variable models, we often alternate between and .