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CSDA
2007
264views more  CSDA 2007»
11 years 11 months ago
Model-based methods to identify multiple cluster structures in a data set
Model-based clustering exploits finite mixture models for detecting group in a data set. It provides a sound statistical framework which can address some important issues, such as...
Giuliano Galimberti, Gabriele Soffritti
BMCBI
2008
118views more  BMCBI 2008»
11 years 12 months ago
Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling
Background: With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few appro...
Grace S. Shieh, Chung-Ming Chen, Ching-Yun Yu, Jui...
NIPS
1996
12 years 1 months ago
Continuous Sigmoidal Belief Networks Trained using Slice Sampling
Real-valued random hidden variables can be useful for modelling latent structure that explains correlations among observed variables. I propose a simple unit that adds zero-mean G...
Brendan J. Frey
NIPS
2000
12 years 1 months ago
Discovering Hidden Variables: A Structure-Based Approach
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As s...
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koll...
UAI
2004
12 years 1 months ago
Convolutional Factor Graphs as Probabilistic Models
Based on a recent development in the area of error control coding, we introduce the notion of convolutional factor graphs (CFGs) as a new class of probabilistic graphical models. ...
Yongyi Mao, Frank R. Kschischang, Brendan J. Frey
UAI
2001
12 years 1 months ago
Learning the Dimensionality of Hidden Variables
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Dete...
Gal Elidan, Nir Friedman
UAI
2001
12 years 1 months ago
Linearity Properties of Bayes Nets with Binary Variables
It is "well known" that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved ca...
David Danks, Clark Glymour
SUTC
2006
IEEE
12 years 5 months ago
Detection and Repair of Software Errors in Hierarchical Sensor Networks
Abstract— Sensor networks are being increasingly deployed for collecting critical data in various applications. Once deployed, a sensor network may experience faults at the indiv...
Douglas Herbert, Yung-Hsiang Lu, Saurabh Bagchi, Z...
PKDD
2009
Springer
155views Data Mining» more  PKDD 2009»
12 years 6 months ago
Dynamic Factor Graphs for Time Series Modeling
Abstract. This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities betwee...
Piotr W. Mirowski, Yann LeCun
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