Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TPAMI.2014.2318711 | Pattern Analysis and Machine Intelligence, IEEE Transactions |
Keywords | Field | DocType |
gaussian processes,variational bayes,gene expression,structured time series,time series analysis,optimization,data models,computational modeling,vectors | Time series,Data modeling,Normal distribution,Computer science,Artificial intelligence,Gaussian process,Cluster analysis,Dirichlet process,Pattern recognition,Inference,Algorithm,Nonparametric statistics,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 2 | 0162-8828 |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Hensman | 1 | 265 | 20.05 |
| Magnus Rattray | 2 | 1 | 0.35 |
| Neil D. Lawrence | 3 | 1 | 0.69 |