Sunday, May 17, 2015

Patrick Stinson: May 20th

Abstract: I'll present Lindsten and Schoen's review of SMC-based backward simulation methods. The most immediate application of backward simulation is to address state smoothing problems in sequential models; however, this method can be generalized to non-Markovian latent variable models. Particle MCMC is a new method that incorporates SMC-based proposal schemes into MCMC algorithms. Backward simulation and a related method, ancestral sampling, can dramatically increase particle efficiency and mixing in this setting. Paper: "Backward Simulation Methods for Monte Carlo Statistical Inference" by Fredrik Lindsten and Thomas B. Schoen Link:

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