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Bayesian Nonparametric Model for Power Disaggregation

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If you have a question about this talk, please contact Zoubin Ghahramani.

We propose an infinite factorial unbounded-state hidden Markov model (HMM) through the construction of a Bayesian nonparametric (BNP) prior over integer-valued matrices (in which each column represents a Markov chain) with the property of presenting an infinite number of columns with an unbounded number of states, namely, IFUHMM . First, we extend the existent infinite factorial binary-state HMM to allow for any number of states, and derive two Markov chain Monte Carlo (MCMC) and a variational inference algorithms. Then, we modify this model to allow an unbounded number of states and derive a new inference algorithm based on MCMC methods that properly deals with the trade-off between the unbounded number of states and chains. Finally, we apply both the infinite factorial (nonbinary) HMM and the IFUHMM to the power disaggregation problem and show their ability to provide more interpretable representations of the data structure than the existing BNP approaches for HMMs.

This talk is part of the Machine Learning @ CUED series.

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