Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data

NeurIPS 2012 Michael C. HughesEmily FoxErik B. Sudderth

Applications of Bayesian nonparametric methods require learning and inference algorithms which efficiently explore models of unbounded complexity. We develop new Markov chain Monte Carlo methods for the beta process hidden Markov model (BP-HMM), enabling discovery of shared activity patterns in large video and motion capture databases... (read more)

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