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ICML
2000
IEEE
15 years 2 months ago
A Bayesian Framework for Reinforcement Learning
The reinforcement learning problem can be decomposed into two parallel types of inference: (i) estimating the parameters of a model for the underlying process; (ii) determining be...
Malcolm J. A. Strens
NIPS
2008
14 years 11 months ago
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switc...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
90
Voted
AAAI
2004
14 years 11 months ago
Bayesian Inference on Principal Component Analysis Using Reversible Jump Markov Chain Monte Carlo
Based on the probabilistic reformulation of principal component analysis (PCA), we consider the problem of determining the number of principal components as a model selection prob...
Zhihua Zhang, Kap Luk Chan, James T. Kwok, Dit-Yan...
101
Voted
ESANN
2006
14 years 11 months ago
Bayesian source separation: beyond PCA and ICA
Blind source separation (BSS) has become one of the major signal and image processing area in many applications. Principal component analysis (PCA) and Independent component analys...
Ali Mohammad-Djafari
93
Voted
ACL
2012
13 years 17 days ago
Semantic Parsing with Bayesian Tree Transducers
Many semantic parsing models use tree transformations to map between natural language and meaning representation. However, while tree transformations are central to several state-...
Bevan K. Jones, Mark Johnson, Sharon Goldwater