MCMC (Markov chain Monte Carlo) is a family of methods that are applied in computational physics and chemistry and also widely used in bayesian machine learning. It is used to simulate physical systems with Gibbs canonical distribution: $$ p(\vx) \propto \exp\left( - \frac{U(\vx)}{T} \right) $$ Probability `$ p(\vx) $` of a system to be in the state `$ \vx $` depends on the energy of the state `$U
![Hamiltonian Monte Carlo explained by Alex Rogozhnikov](https://cdn-ak-scissors.b.st-hatena.com/image/square/bb54dab48f34d588b382c20407588e5bf7ab3915/height=288;version=1;width=512/http%3A%2F%2Farogozhnikov.github.io%2Fimages%2Fml_demonstrations%2Fhmc_explained.png)