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Discrete Sampling using Semigradient-based Product Mixtures
[article]
2018
arXiv
pre-print
We consider the problem of inference in discrete probabilistic models, that is, distributions over subsets of a finite ground set. These encompass a range of well-known models in machine learning, such as determinantal point processes and Ising models. Locally-moving Markov chain Monte Carlo algorithms, such as the Gibbs sampler, are commonly used for inference in such models, but their convergence is, at times, prohibitively slow. This is often caused by state-space bottlenecks that greatly
arXiv:1807.01808v2
fatcat:xeuzzt72xnf2bjdre46vrctohq