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Sequential Monte Carlo methods for graphical models

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Advanced Monte Carlo Methods for Complex Inference Problems

Co-authors: Christian A. Naesseth (Linkoping University), Fredrik Lindsten (University of Cambridge)

We develop a sequential Monte Carlo (SMC) algorithm for inference in general probabilistic graphical model. Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. By targeting these auxiliary distributions using purpose built SMC samplers we are able to approximate the full joint distribution defined by the graphical model. Our SMC sampler also provides an unbiased estimate of the partition function (normalization constant) and we show how it can be used within a particle Markov chain Monte Carlo framework. This allows for better approximations of the marginals and for unknown parameters to be estimated. The proposed inference algorithms can deal with an arbitrary graph structure and the domain of the random variables in the graph can be discrete or continuous.

Related Links: – Associated paper – Speaker (Thomas Schn) home page

This talk is part of the Isaac Newton Institute Seminar Series series.

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