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» Hierarchical Gaussian process latent variable models
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CSDA
2011
15 years 1 months ago
Approximate forward-backward algorithm for a switching linear Gaussian model
Motivated by the application of seismic inversion in the petroleum industry we consider a hidden Markov model with two hidden layers. The bottom layer is a Markov chain and given ...
Hugo Hammer, Håkon Tjelmeland
ICIP
2001
IEEE
16 years 8 months ago
EM algorithms of Gaussian mixture model and hidden Markov model
The HMM (Hidden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian Mixture Mo...
Guorong Xuan, Wei Zhang, Peiqi Chai
UAI
2004
15 years 8 months ago
A Hierarchical Graphical Model for Record Linkage
The task of matching co-referent records is known among other names as record linkage. For large record-linkage problems, often there is little or no labeled data available, but u...
Pradeep D. Ravikumar, William W. Cohen
PRICAI
2000
Springer
15 years 10 months ago
Generating Hierarchical Structure in Reinforcement Learning from State Variables
This paper presents the CQ algorithm which decomposes and solves a Markov Decision Process (MDP) by automatically generating a hierarchy of smaller MDPs using state variables. The ...
Bernhard Hengst
BMCBI
2011
14 years 10 months ago
A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression
Background: The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) ...
Alfredo A. Kalaitzis, Neil D. Lawrence