Now showing items 1-6 of 6
Beta-negative binomial process and poisson factor analysis
(Journal of Machine Learning Research, 2012-01-01)
© Copyright 2012 by the authors. A beta-negative binomial (BNB) process is proposed, leading to a beta-gamma-Poisson process, which may be viewed as a "multiscoop" generalization of the beta-Bernoulli process. The BNB process ...
Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling
(IEEE Transactions on Biomedical Engineering, 2014-01-01)
We propose a methodology for joint feature learning and clustering of multichannel extracellular electrophysiological data, across multiple recording periods for action potential detection and classification (sorting). Our ...
Lognormal and gamma mixed negative binomial regression
(Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2012-10-10)
In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative ...
Dynamic nonparametric bayesian models for analysis of music
(Journal of the American Statistical Association, 2010-06-01)
The dynamic hierarchical Dirichlet process (dHDP) is developed to model complex sequential data, with a focus on audio signals from music. The music is represented in terms of a sequence of discrete observations, and the ...
Bayesian Gaussian Copula Factor Models for Mixed Data.
(J Am Stat Assoc, 2013-06-01)
Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent ...
Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.
(BMC Bioinformatics, 2010-11-09)
BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques ...