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NIPS
1992
15 years 1 months ago
Hidden Markov Model} Induction by Bayesian Model Merging
This paper describes a technique for learning both the number of states and the topologyof Hidden Markov Models from examples. The inductionprocess starts with the most specific m...
Andreas Stolcke, Stephen M. Omohundro
116
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ICML
2010
IEEE
15 years 1 months ago
Bayesian Nonparametric Matrix Factorization for Recorded Music
Recent research in machine learning has focused on breaking audio spectrograms into separate sources of sound using latent variable decompositions. These methods require that the ...
Matthew D. Hoffman, David M. Blei, Perry R. Cook
130
Voted
IDA
2006
Springer
15 years 21 days ago
Temporal Bayesian classifiers for modelling muscular dystrophy expression data
The analysis of microarray data from time-series experiments requires specialised algorithms, which take the temporal ordering of the data into account. In this paper we explore a ...
Allan Tucker, Peter A. C. 't Hoen, Veronica Vincio...
99
Voted
TIP
2008
116views more  TIP 2008»
15 years 18 days ago
Variational Bayesian Image Restoration Based on a Product of t-Distributions Image Prior
Image priors based on products have been recognized to offer many advantages because they allow simultaneous enforcement of multiple constraints. However, they are inconvenient for...
Giannis K. Chantas, Nikolas P. Galatsanos, Aristid...
103
Voted
JMLR
2010
155views more  JMLR 2010»
14 years 7 months ago
Bayesian Gaussian Process Latent Variable Model
We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method...
Michalis Titsias, Neil D. Lawrence