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What is Metropolis-Hastings Algorithm Explained about?

The document describes the Metropolis-Hastings algorithm for sampling from a target probability distribution. It consists of constructing a Markov chain whose stationary distribution is the target distribution. At each step, a candidate sample is drawn from a proposal distribution and either accepted or rejected based on an acceptance ratio. The document provides an example implementation in R of a random walk Metropolis-Hastings algorithm to sample from a posterior distribution with a normal likelihood and

Author

Mingyu Liu

Language

EN