Understanding the “Antoniak equation” The equation I call the “Antoniak equation” (due to Antoniak, 1974) computes the probability that N samples from a Dirichlet process with concentration parameter α will draw

Source URL: www.cs.cmu.edu

1 Background Subtraction with Dirichlet Process Mixture Models Tom S. F. Haines and Tao Xiang Abstract—Video analysis often begins with background subtraction. This problem is often approached in two steps - a backgro

Source URL: thaines.com

Dirichlet Distribution, Dirichlet Process and Dirichlet Process Mixture Leon Gu CSD, CMU Binomial and Multinomial

Source URL: www.cs.cmu.edu

Copyright Ó 2007 by the Genetics Society of America DOI: geneticsInference of Population Structure Under a Dirichlet Process Model John P. Huelsenbeck*,1 and Peter Andolfatto† *Department of Integr

Source URL: genomics.princeton.edu

The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling Sinead Williamson Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, UK

Source URL: www.gatsby.ucl.ac.uk

Exchangeable Inconsistent Priors for Bayesian Posterior Inference Max Welling Ian Porteous

Source URL: www.ics.uci.edu

• Dirichlet process
• Poisson distribution
• Principle of maximum entropy
• Prior probability
• Normal distribution
• Mixture model
• Gibbs sampling
• Empirical Bayes method
• Bayesian inference
• Statistics
• Probability and statistics
• Bayesian statistics

Incremental Learning of Nonparametric Bayesian Mixture Models Ryan Gomes∗ Max Welling† ∗

Source URL: www.ics.uci.edu

• Cluster analysis
• Data analysis
• Estimation theory
• Expectation–maximization algorithm
• Dirichlet process
• Variational Bayesian methods
• Mixture model
• Principal component analysis
• Kernel principal component analysis
• Statistics
• Multivariate statistics
• Machine learning

An Alternative Prior Process for Nonparametric Bayesian Clustering Hanna M. Wallach Department of Computer Science University of Massachusetts Amherst

Source URL: www.gatsby.ucl.ac.uk

• Pitman–Yor process
• Chinese restaurant process
• Dirichlet distribution
• Concentration parameter
• Mixture model
• Bayesian inference
• Prior probability
• Statistics
• Stochastic processes
• Dirichlet process

Infinite State Bayesian Networks Max Welling∗, Ian Porteous, Evgeniy Bart† Donald Bren School of Information and Computer Sciences University of California Irvine Irvine, CAUSA {welling,iporteou}@ics.uci.

Source URL: www.ics.uci.edu

• Statistical natural language processing
• Markov models
• Graphical models
• Variational Bayesian methods
• Dirichlet process
• Gibbs sampling
• Bayesian network
• Latent Dirichlet allocation
• Mixture model
• Statistics
• Bayesian statistics
• Statistical models

Journal of Machine Learning Research xx-xx Submitted 6/08; Published xx/xx Distributed Algorithms for Topic Models David Newman

Source URL: www.ics.uci.edu

• Latent Dirichlet allocation
• Statistical classification
• Bayesian networks
• Topic model
• Mixture model
• Dirichlet process
• Linear discriminant analysis
• Algorithm
• Gibbs sampling
• Statistics
• Statistical natural language processing
• Statistical models