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

Source URL: www-stat.wharton.upenn.edu

• Statistics
• Stochastic processes
• Statistical theory
• Probability
• Hierarchical Dirichlet process
• Dirichlet process
• PitmanYor process
• Chinese restaurant process
• Dirichlet distribution
• Mixture model
• Concentration parameter
• Pattern recognition

Inference of functional clustering patterns from non-functional data Long Nguyen Department of Statistics University of Michigan

Source URL: dept.stat.lsa.umich.edu

• Statistics
• Statistical theory
• Probability
• Probability distributions
• Stochastic processes
• Dirichlet process
• Bayesian statistics
• Markov chain Monte Carlo
• Mixture distribution
• Statistical inference
• Concentration parameter
• Mixture model

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

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

Active Learning with Constrained Topic Model Yi Yang Northwestern University Shimei Pan

Source URL: nlp.stanford.edu

• Latent Dirichlet allocation
• Science
• Relevance
• Concentration parameter
• Dynamic topic model
• Statistical natural language processing
• Statistics
• Topic model

Curiosity Based Exploration for Learning Terrain Models Yogesh Girdhar, David Whitney, and Gregory Dudek Abstract— We present a robotic exploration technique in which the goal is to learn a visual model that can be use

Source URL: www.cim.mcgill.ca

• Probabilistic latent semantic analysis
• Perplexity
• Latent Dirichlet allocation
• Robot
• Concentration parameter
• Topic–comment
• Statistical natural language processing
• Statistics
• Topic model

Nonparametric Bayesian Methods for Large Scale Multi-Target Tracking Emily B. Fox David S. Choi

Source URL: www.stat.washington.edu

• Markov models
• Dirichlet process
• Mixture model
• Dirichlet distribution
• Markov chain
• Concentration parameter
• Gibbs sampling
• Poisson distribution
• Bayesian inference
• Statistics
• Stochastic processes
• Bayesian statistics

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.cs.columbia.edu

• Dirichlet process
• Latent Dirichlet allocation
• Dirichlet distribution
• Mixture model
• Hidden Markov model
• Concentration parameter
• Normal distribution
• Topic model
• Golden ratio
• Statistics
• Probability and statistics
• Statistical natural language processing

Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process Chong Wang Computer Science Department Princeton University

Source URL: www.cs.columbia.edu

• Statistical inference
• Latent Dirichlet allocation
• Dirichlet process
• Dirichlet distribution
• Concentration parameter
• Mixture model
• Topic model
• Multinomial distribution
• Bayesian inference
• Statistics
• Statistical natural language processing
• Bayesian statistics

230 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 37,

Source URL: users.cecs.anu.edu.au

• Stochastic processes
• Dirichlet process
• Concentration parameter
• Mixture model
• Topic model
• Probabilistic latent semantic analysis
• Pitman–Yor process
• N-gram
• Language model
• Statistics
• Probability and statistics
• Statistical natural language processing