Markov Chain Monte Carlo (MCMC) is a computational method used for simulating complex stochastic systems. It is a statistical technique that allows one to sample from complicated probability distributions, which may be difficult to sample from directly. MCMC works by constructing a Markov chain that has the target distribution of interest as its stationary distribution. By iteratively sampling from this chain and discarding the initial "burn-in" period, one can obtain samples that approximate the desired distribution. MCMC has a wide range of applications in statistics, machine learning, physics, biology, and other fields where complex probabilistic models need to be analyzed. It is particularly useful when analytical solutions are difficult or impossible to obtain, as it provides a way to approximate the desired distribution through simulation and sampling.