Markov Chain Monte Carlo

Markov Chain Monte Carlo (MCMC) is a method used to estimate complex probabilities or distributions by generating a sequence of samples. It works by making small random moves through possible states, with each move depending only on the current state (a Markov process). Over time, the samples tend to reflect the true distribution, allowing us to understand the likelihood of different outcomes without calculating everything directly. MCMC is widely used in statistics, machine learning, and scientific research to analyze data and solve problems involving uncertainty.