Bayesian Hierarchical Clustering Katherine A. Heller Zoubin Ghahramani Gatsby Computational Neuroscience Unit, University College London 17 Queen Square, London, WC1N 3AR, UK

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

• Cluster analysis
• Bioinformatics
• Data mining
• Hierarchical clustering
• Single-linkage clustering
• Conjugate prior
• Mixture model
• Dirichlet process
• Variational Bayesian methods
• Statistics
• Machine learning
• Bayesian statistics

The Time-Marginalized Coalescent Prior for Hierarchical Clustering Max Welling Department of Computer Science University of California, Irvine

Source URL: www.ics.uci.edu

• Cluster analysis
• Markov models
• Bayesian statistics
• Coalescent theory
• Mixture model
• Dirichlet process
• Markov chain
• Prior probability
• Statistics
• Probability and statistics
• Probability

Efficient Bayesian Methods for Clustering Katherine Ann Heller B.S., Computer Science, Applied Mathematics and Statistics, State University of New York at Stony Brook, USAM.S., Computer Science, Columbia Universi

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

• Machine learning
• Cluster analysis
• Data mining
• Mixture model
• Graphical model
• Hierarchical clustering
• Bayesian inference
• Dirichlet process
• Content-based image retrieval
• Statistics
• Bayesian statistics
• Statistical models

Unsupervised Learning of Visual Taxonomies Evgeniy Bart Caltech Pasadena, CAIan Porteous

Source URL: www.ics.uci.edu

• Statistical natural language processing
• Statistical models
• Computer vision
• Object recognition
• Latent Dirichlet allocation
• One-shot learning
• Mixture model
• Dirichlet process
• Graphical model
• Statistics
• Artificial intelligence
• Machine learning

Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation James Foulds Levi Boyles

Source URL: www.ics.uci.edu

• Expectation–maximization algorithm
• Variational Bayesian methods
• Topic model
• Algorithm
• Gibbs sampling
• Natural language processing
• Dirichlet process
• Mathematical optimization
• Dynamic topic model
• Statistics
• Statistical natural language processing
• Latent Dirichlet allocation

Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures Ian Porteous and Arthur Asuncion and Max Welling Bren School of Information and Computer Science University of California Irvine Irvine,

Source URL: www.ics.uci.edu

Tree-Based Inference for Dirichlet Process Mixtures Yang Xu Machine Learning Department School of Computer Science Carnegie Mellon University

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

Hierarchical Learning of Dimensional Biases in Human Categorization Adam Sanborn Gatsby Computational Neuroscience Unit University College London

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

• Learning theory
• Cluster analysis
• Data analysis
• Mixture model
• Dimensional analysis
• Dimension
• Multivariate normal distribution
• Dirichlet process
• Covariance matrix
• Statistics
• Machine learning
• Concept learning

Memory Bounded Inference in Topic Models Ryan Gomes GOMES @ VISION . CALTECH . EDU Dept. of Computation and Neural Systems, California Institute of Technology, Pasadena, CAUSA Max Welling

Source URL: www.ics.uci.edu

• Estimation theory
• Expectation–maximization algorithm
• Missing data
• Mixture model
• Latent Dirichlet allocation
• Dirichlet process
• Time complexity
• Calculus of variations
• Statistics
• Theoretical computer science
• Statistical natural language processing

Collapsed Variational Dirichlet Process Mixture Models∗ Max Welling Kenichi Kurihara Dept. of Computer Science Dept. of Computer Science UC Irvine, USA

Source URL: www.ics.uci.edu

• Expectation–maximization algorithm
• Dirichlet process
• Mixture model
• Latent Dirichlet allocation
• Normal distribution
• Factorial
• Markov chain
• Central limit theorem
• Bayesian inference
• Statistics
• Bayesian statistics
• Variational Bayesian methods