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MCMC for continuous-time discrete-state systems

lib:d6bc6aadaaa14f36 (v1.0.0)

Authors: Vinayak Rao,Yee W. Teh
Where published: NeurIPS 2012 12
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Abstract URL: http://papers.nips.cc/paper/4746-mcmc-for-continuous-time-discrete-state-systems


We propose a simple and novel framework for MCMC inference in continuous-time discrete-state systems with pure jump trajectories. We construct an exact MCMC sampler for such systems by alternately sampling a random discretization of time given a trajectory of the system, and then a new trajectory given the discretization. The first step can be performed efficiently using properties of the Poisson process, while the second step can avail of discrete-time MCMC techniques based on the forward-backward algorithm. We compare our approach to particle MCMC and a uniformization-based sampler, and show its advantages.

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