Research output: Contribution to Journal/Magazine › Journal article › peer-review
Article number | 6802369 |
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<mark>Journal publication date</mark> | 1/02/2015 |
<mark>Journal</mark> | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Issue number | 2 |
Volume | 37 |
Number of pages | 11 |
Pages (from-to) | 383-393 |
Publication Status | Published |
Early online date | 18/04/14 |
<mark>Original language</mark> | English |
In this publication, we combine two Bayesian nonparametric 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 variational approximation 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 significant speed-up over EM-based variational inference.