UCL ELLIS

Speaker	Vinayak Rao
Affiliation	UCL
Date	Friday, 13 July 2012
Time	12:30-14:00
Location	Zoom
Link	TBA
Event series	DeepMind/ELLIS CSML Seminar Series
Abstract	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. Joint work with Yee Whye Teh. Slides for the talk: PDF
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