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Theoretical guarantees in Bayesian non parametric hidden Markov models

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Hidden Markov models (HMMs) have been widely used in diverse fields such as speech recognition, genomics or econometrics. Because parametric modelling of HMMs may lead to poor results in practice recent interest in using non parametric HMMs appeared in applications. Yet little thoughts have been given to theory in this framework. In this talk I will first define and describe the model I’m interested in, namely Bayesian non parametric hidden Markov models. I will then present the problems I study in these models (namely concerning the asymptotic behaviour of the posterior distribution). I will finally give some theoretical guarantees I have obtained in these models.

This talk is part of the Cambridge Analysts' Knowledge Exchange series.

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