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» Bayesian Approaches to Gaussian Mixture Modeling
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CSDA
2006
142views more  CSDA 2006»
13 years 9 months ago
Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models
Fitting of non-Gaussian hierarchical random effects models by approximate maximum likelihood can be made automatic to the same extent that Bayesian model fitting can be automated ...
Hans J. Skaug, David A. Fournier
KDD
2004
ACM
170views Data Mining» more  KDD 2004»
14 years 2 months ago
Estimating the size of the telephone universe: a Bayesian Mark-recapture approach
Mark-recapture models have for many years been used to estimate the unknown sizes of animal and bird populations. In this article we adapt a finite mixture mark-recapture model i...
David Poole
BMCBI
2006
103views more  BMCBI 2006»
13 years 9 months ago
Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression
Background: The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level ...
Sébastien Lemieux
ICASSP
2007
IEEE
14 years 3 months ago
Spatial Mixture Modelling for the Joint Detection-Estimation of Brain Activity in fMRI
— Within-subject analysis in event-related functional Magnetic Resonance Imaging (fMRI) first relies on (i) a detection step to localize which parts of the brain are activated b...
Thomas Vincent, Philippe Ciuciu, Jérô...
NIPS
2003
13 years 10 months ago
Gene Expression Clustering with Functional Mixture Models
We propose a functional mixture model for simultaneous clustering and alignment of sets of curves measured on a discrete time grid. The model is specifically tailored to gene exp...
Darya Chudova, Christopher E. Hart, Eric Mjolsness...