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» Nonparametric statistical inference for ergodic processes
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NIPS
2008
13 years 6 months ago
Analyzing human feature learning as nonparametric Bayesian inference
Almost all successful machine learning algorithms and cognitive models require powerful representations capturing the features that are relevant to a particular problem. We draw o...
Joseph Austerweil, Thomas L. Griffiths
ICML
2004
IEEE
14 years 6 months ago
Variational methods for the Dirichlet process
Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While ...
David M. Blei, Michael I. Jordan
JCB
2007
191views more  JCB 2007»
13 years 5 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
KDD
2010
ACM
233views Data Mining» more  KDD 2010»
13 years 9 months ago
Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora
Mining cluster evolution from multiple correlated time-varying text corpora is important in exploratory text analytics. In this paper, we propose an approach called evolutionary h...
Jianwen Zhang, Yangqiu Song, Changshui Zhang, Shix...
CVPR
2012
IEEE
11 years 7 months ago
Nonparametric discovery of activity patterns from video collections
We propose a nonparametric framework based on the beta process for discovering temporal patterns within a heterogenous video collection. Starting from quantized local motion descr...
Michael C. Hughes, Erik B. Sudderth