AI engineering glossary · Probability
What is MCMC (Markov chain Monte Carlo)?
Markov chain Monte Carlo (also called MCMC): A family of methods that draw samples from a complex distribution by building a Markov chain whose long-run distribution is the target. It is widely used in Bayesian inference when the posterior cannot be computed directly.
Markov chain Monte Carlo: an example
The Metropolis-Hastings algorithm proposes a move, accepts or rejects it by a probability rule, and after many steps its visited states follow the target distribution.
Related terms
More Probability terms
- Markov property
- Markov's inequality
- Maximum likelihood estimation
- Median
- Memoryless property
- Mode
- Monte Carlo simulation
- Monty Hall problem
Where you learn it
Markov chain Monte Carlo is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.