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Efficient MCMC for Continuous Time Discrete State Systems

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If you have a question about this talk, please contact Zoubin Ghahramani.

A variety of phenomena are best described using dynamical models which operate on a discrete state space and in continuous time. Examples include Markov jump processes, continuous time Bayesian networks, renewal processes and other point processes, with applications ranging from systems biology, genetics, computing networks and human-computer interactions. Posterior computations typically involve approximations like time discretization and can be computationally intensive. In this talk I will describe recent work on a class of Markov chain Monte Carlo methods that allow efficient computations while still being exact. The core idea is to use an auxiliary variable Gibbs sampler based on uniformization, a representation of a continuous time dynamical system as a Markov chain operating over a discrete set of points drawn from a Poisson process. This is joint work with Yee Whye Teh. If time permits, I shall also talk about some recent work on spatial point processes with David Dunson.

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

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