Sequential Monte Carlo for Undirected Models

by Firas Hamze

I will be discussing my work on sequential Monte Carlo (SMC) for undirected graphical models. The objects are (1) to approximate integrals of functions of the variables, of which the marginal probabilities used in inference are a special case, and (2) to approximate the normalization constant of the model. The talk will first introduce the models, including the familiar Markov Random Fields used in computer vision, and then discuss a more general approach to SMC than the one we may be accustomed to seeing in the context of particle filtering. I will then summarize my sequential edge-coupling algorithm and its relation to annealing-based methods, and its distinction from Markov Chain Monte Carlo (MCMC).

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