Bayesian Approximations to Hidden Semi-Markov Models

06/16/2020 ∙ by Beniamino Hadj-Amar, et al. ∙ 0

We propose a Bayesian hidden Markov model for analyzing time series and sequential data where a special structure of the transition probability matrix is embedded to model explicit-duration semi-Markovian dynamics. Our formulation allows for the development of highly flexible and interpretable models that can integrate available prior information on state durations while keeping a moderate computational cost to perform efficient posterior inference. We show the benefits of choosing a Bayesian approach over its frequentist counterpart, in terms of incorporation of prior information, quantification of uncertainty, model selection and out-of-sample forecasting. The use of our methodology is illustrated in an application relevant to e-Health, where we investigate rest-activity rhythms using telemetric activity data collected via a wearable sensing device.



There are no comments yet.


page 21

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.