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Decayed MCMC for probabilistic filtering

Google Tech TalksMarch, 28 2008ABSTRACTBhaskara M. Marthi - Research Scientist I will describe an algorithm for probabilistic filtering, the problem of maintaining a probability distribution over the hidden state of a dynamical system given periodic noisy observations. This problem appears in various guises in practice, such as activity monitoring, state estimation, visual tracking, and fault diagnosis. Our algorithm, known as decayed MCMC, scales better than exact methods on many problems, and is less susceptible to losing track of the mode than the popular sequential Monte Carlo or particle filtering methods. Standard Markov chain Monte-Carlo mixing time analyses are insufficient to bound the complexity of our algorithm, and so we extend them to the setting of convergence of a marginal distribution. Speaker: Dr. Bhaskara M. Marthi - Research ScientistDr. Bhaskara Marthi is currently a postdoctoral research associate at MIT, working with Leslie Kaelbling and Tomas Lozano Perez on hierarchical planning and robotic manipulation. He received his PhD in 2006 from the University of California, Berkeley, working with Stuart Russell on reinforcement learning with partial programs, and its application to AI design for large real-time strategy video games. His other interests include probabilistic reasoning, relational and first-order models, and Monte Carlo algorithms.
Length: 47:38


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