In machine learning, Langevin dynamics refers to a 🎯 Markov Chain Monte Carlo method that uses the above equation to sample from distribution . Specifically, our transition is
where . is sampled from an arbitrary prior distribution, and as approaches and the number of iterations approaches infinity, converges to a sample from . Note that the only term we need in the transition step is the score function , which can be trained via 🎼 Score Matching.