Institute of Electrical and Electronics Engineers, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(37), p. 383-393, 2015
DOI: 10.1109/tpami.2014.2318711
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In this publication, we combine two Bayesian non-parametric models: the Gaussian Process (GP) and the Dirichlet Process (DP). Our innovation in the GP model is to introduce a variation on the GP prior which enables us to model structured time-series data, i.e. data containing groups where we wish to model inter- and intra-group variability. Our innovation in the DP model is an implementation of a new fast collapsed variational inference procedure which enables us to optimize our variationala pproximation significantly faster than standard VB approaches. In a biological time series application we show how our model better captures salient features of the data, leading to better consistency with existing biological classifications, while the associated inference algorithm provides a twofold speed-up over EM-based variational inference. ; Comment: Accepted for publication in special edition of TPAMI on Bayesian Nonparametrics