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Yee Whye Teh (University College London)

17 November 2011 @ 12:00

 

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Date:
17 November 2011
Time:
12:00
Event Category:

Efficient MCMC for Continuous Time Discrete State Systems

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, neuroscience, 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 our 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 subordinated to a
Poisson process.