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» Nonparametric factor analysis with beta process priors
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JCB
2007
191views more  JCB 2007»
13 years 7 months ago
Bayesian Haplotype Inference via the Dirichlet Process
The problem of inferring haplotypes from genotypes of single nucleotide polymorphisms (SNPs) is essential for the understanding of genetic variation within and among populations, ...
Eric P. Xing, Michael I. Jordan, Roded Sharan
JMLR
2010
150views more  JMLR 2010»
13 years 1 months ago
Supervised Dimension Reduction Using Bayesian Mixture Modeling
We develop a Bayesian framework for supervised dimension reduction using a flexible nonparametric Bayesian mixture modeling approach. Our method retrieves the dimension reduction ...
Kai Mao, Feng Liang, Sayan Mukherjee
ISCA
2010
IEEE
219views Hardware» more  ISCA 2010»
14 years 3 days ago
Using hardware vulnerability factors to enhance AVF analysis
Fault tolerance is now a primary design constraint for all major microprocessors. One step in determining a processor’s compliance to its failure rate target is measuring the Ar...
Vilas Sridharan, David R. Kaeli
ICASSP
2011
IEEE
12 years 10 months ago
Infinite-state spectrum model for music signal analysis
This paper presents a nonparametric Bayesian extension of nonnegative matrix factorization (NMF) for music signal analysis. Instrument sounds often exhibit non-stationary spectral...
Masahiro Nakano, Jonathan Le Roux, Hirokazu Kameok...
ICASSP
2011
IEEE
12 years 10 months ago
On selecting the hyperparameters of the DPM models for the density estimation of observation errors
The Dirichlet Process Mixture (DPM) models represent an attractive approach to modeling latent distributions parametrically. In DPM models the Dirichlet process (DP) is applied es...
Asma Rabaoui, Nicolas Viandier, Juliette Marais, E...