Gibbs Sampling is a different approach to sampling where we generate samples by making a random change to the previous sample.
- Select an arbitrary state in the Bayesian network. Pick state with evidence variables fixed at observed values. e.g. if we know Cloudy is true, we pick that.
- Generate next state by randomly sampling from a non-evidence value.
- Do sampling conditional on current values of Markov blanket.