Explain Markov Chain Monte Carlo methods. Why are they necessary in Bayesian inference, and what are common algorithms like Metropolis-Hastings and Gibbs sampling?

Sample answer preview

Markov Chain Monte Carlo methods are a class of algorithms for sampling from probability distributions that are difficult to sample from directly. They are essential in Bayesian inference because computing posterior distributions analytically is often impossible or intractable,…