Infinite Markov Models and Related Topics / Unknown Targets and Patterns of Dynamics

By Daichi Mochihashi

In the first part of this talk, I will introduce the Infinite Markov Model, a nonparametric Bayesian Markov model that allows us to infer latent Markov orders purely from the observations. [I will also include slides not discussed during my talk at NIPS 2007 and some research directions.]

The second part of this talk will describe our group's latest work (in submission to CVPR 2008) about time series inference. This model infers a time-varying number of moving target trajectories and patterns of their dynamics simultaneously. Patterns of dynamics are modeled through a Dirichlet process mixtures of Kalman filters, and the inference is performed through Particle filters.

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