Markov Chain Monte Carlo
Markov Chain Monte Carlo (MCMC) is a computational method used to simulate complex probability distributions. It is a type of Monte Carlo method that uses Markov chains to generate a sequence of samples from a target distribution. MCMC is particularly useful when the target distribution is high-dimensional and difficult to sample directly. The method works by constructing a Markov chain that has the target distribution as its equilibrium distribution. The chain is then run for a large number of iterations, and the resulting samples are used to estimate properties of the target distribution, such as its mean, variance, and higher moments. MCMC has many applications in statistics, physics, chemistry, and engineering, and is widely used in Bayesian inference, where it is used to sample from the posterior distribution of model parameters given observed data.
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