Metropolis–-Hastings (MH) algorithm 


Metropolis–-Hastings (MH) algorithm search for term

The Metropolis–Hastings (MH) algorithm is similar to the MC simulation procedure in that it aims to sample from a stationary Markov chain to simulate observations from a probability distribution.However, in this case, rather than simulating independent observations from the stationary distribution, it simulates sequential values from the chain until it converges and then samples simulated values at intervals from the chain to mimic independent samples from the stationary distribution. The MH algorithm has the advantage that it can improve the efficiency of simulations when the state space is large because it focuses the simulated variables on values with high probability in the stationary chain. Disadvantages include the fact that in most practical applications, there are no rigorous methods available to determine when the chain has converged or what the optimal intervals between samples are to extract the most information while preserving independence between observations. (Beaumont 2004)