Metropolis-Hastings
Metropolis-Hastings is a Markov chain Monte Carlo (MCMC) algorithm used for sampling from probability distributions, particularly in Bayesian statistics and computational physics. It generates a sequence of random samples from a target distribution by proposing new states and accepting or rejecting them based on an acceptance probability, allowing exploration of complex, high-dimensional spaces where direct sampling is infeasible.
Developers should learn Metropolis-Hastings when working on Bayesian inference, machine learning models with intractable posteriors, or simulations in fields like physics and finance. It is essential for tasks such as parameter estimation, uncertainty quantification, and probabilistic programming, where exact sampling methods are computationally prohibitive or impossible.