In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution, one can construct a Markov chain whose elements’ distribution approximates it – that is, the Markov chain’s equilibrium distribution matches the target distribution. The more steps that are included, the more closely the distribution of the sample matches the actual desired distribution.
This library contains the following notes on MCMC methods
- [[mcmc-fundamentals]]
- Metropolis-Hastings Algorithm
- Hamiltonian Monte-Carlo
- [[gibbs-sampler]]