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An introduction to Sequential Monte Carlo

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Sequential Monte Carlo (SMC) methods are a set of sampling techniques for simulating and estimating posterior distributions. In this talk we give an introduction to SMC methods and examples of their applications. We review the classical posterior inference algorithms for state space models, as well as a contemporary generic SMC algorithm for sampling from a sequence of bridging distributions of increasing dimensions. This introduction will be followed by a discussion of different applications of SMC , including particle Markov chain Monte Carlo and particle learning of Gaussian process models.

This talk is part of the Machine Learning Reading Group @ CUED series.

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