1183 search results - page 29 / 237 » Learning Process Models with Missing Data |
139
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ECCB
2003
IEEE
15 years 6 months ago
2003
IEEE
This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactio...
116
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DAGM
2010
Springer
15 years 2 months ago
2010
Springer
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
135
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ICML
2008
IEEE
16 years 2 months ago
2008
IEEE
Hidden Markov models assume that observations in time series data stem from some hidden process that can be compactly represented as a Markov chain. We generalize this model by as...
104
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ICML
2010
IEEE
15 years 2 months ago
2010
IEEE
We propose a novel dependent hierarchical Pitman-Yor process model for discrete data. An incremental Monte Carlo inference procedure for this model is developed. We show that infe...
125
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ICML
2008
IEEE
16 years 2 months ago
2008
IEEE
Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covaria...