markov chain monte carlo
MCMC is a class of algorithms used for sampling from probability distributions based on constructing a Markov chain that has the desired distribution as its equilibrium distribution. It is widely used in Bayesian inference and complex probabilistic modeling.
- Constrained Sampling for Language Models Should Be Easy: An MCMC Perspective
- Discrete Neural Flow Samplers with Locally Equivariant Transformer
- Generative diffusion for perceptron problems: statistical physics analysis and efficient algorithms
- Parallelizing MCMC Across the Sequence Length
- Sampling by averaging: A multiscale approach to score estimation
- Variational Polya Tree