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Vinayak Rao Purdue University

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Markov Chain Monte Carlo for Continuous-Time Discrete-State Systems
10 September 2015 from 4:00 PM to 5:00 PM
201 Thomas Building
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Vinayak RaoA 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 an auxiliary variable Gibbs sampler that alternately resamples a random discretization of time given the state-trajectory of the system, and then samples a new trajectory given this discretization. For the Markov jump process, our construction is related to the idea of 'uniformization', which we generalize to develop samplers for systems with infinite state spaces, unbounded rates. If time permits, I will also talk on some recent work on inference for stochastic differential equations.

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