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» Mixed state estimation for a linear Gaussian Markov model
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ICASSP
2010
IEEE
13 years 3 months ago
Learning in Gaussian Markov random fields
This paper addresses the problem of state estimation in the case where the prior distribution of the states is not perfectly known but instead is parameterized by some unknown par...
Thomas J. Riedl, Andrew C. Singer, Jun Won Choi
ICMLA
2008
13 years 6 months ago
A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation
Time-series segmentation in the fully unsupervised scenario in which the number of segment-types is a priori unknown is a fundamental problem in many applications. We propose a Ba...
Silvia Chiappa
CORR
2010
Springer
116views Education» more  CORR 2010»
13 years 5 months ago
Estimation with Random Linear Mixing, Belief Propagation and Compressed Sensing
Abstract--We apply Guo and Wang's relaxed belief propagation (BP) method to the estimation of a random vector from linear measurements followed by a componentwise probabilisti...
Sundeep Rangan
ICMCS
2005
IEEE
173views Multimedia» more  ICMCS 2005»
13 years 11 months ago
A Multi-Modal Mixed-State Dynamic Bayesian Network for Robust Meeting Event Recognition from Disturbed Data
In this work we present a novel multi-modal mixed-state dynamic Bayesian network (DBN) for robust meeting event classification. The model uses information from lapel microphones,...
Marc Al-Hames, Gerhard Rigoll
GEOINFO
2007
13 years 6 months ago
Model Selection for a Class of Spatio-temporal Models for Areal Data
Abstract. We present a method to perform model selection based on predictive density in a class of spatio-temporal dynamic generalized linear models for areal data. These models as...
Juan C. Vivar, Marco A. R. Ferreira