Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 37, Issue 2, Pages 383-393Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2014.2318711
Keywords
Variational Bayes; Gaussian processes; structured time series; gene expression
Funding
- Biotechnology and Biological Sciences Research Council [BB/H018123/2] Funding Source: researchfish
- Medical Research Council [MR/K022016/1, MR/K022016/2] Funding Source: researchfish
- Medical Research Council [MR/K022016/1, MR/K022016/2] Funding Source: Medline
- BBSRC [BB/H018123/2] Funding Source: UKRI
- MRC [MR/K022016/2, MR/K022016/1] Funding Source: UKRI
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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.
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