Clustering Mutational Spectra via ClassiŽ cation Likelihood and Markov Chain Monte Carlo Algorithms Mario MEDVEDOVIC, Paul SUCCOP, Rakesh SHUKLA, and Kathleen DIXON We have analyzed a set of 39 mutational spectra of the

Source URL: eh3.uc.edu

• Estimation theory
• Expectation–maximization algorithm
• Missing data
• Cluster analysis
• Gibbs sampling
• Normal distribution
• Multinomial distribution
• Statistics
• Multivariate statistics
• Machine learning

Analytical Prediction of Transitions Probabilities in the Conditional Logit Model

Source URL: repec.iza.org

• Statistical models
• Discrete choice
• Logit
• Multinomial logit
• Logistic regression
• Maximum likelihood
• Statistics
• Regression analysis
• Categorical data

CLUSTERING MULTINOMIAL OBSERVATIONS VIA FINITE AND INFINITE MIXTURE MODELS AND MCMC ALGORITHMS Mario Medvedovic, University of Cincinnati Medical Center Mario Medvedovic, Department of Environmental Health, University of

Source URL: eh3.uc.edu

• Cluster analysis
• Mixture model
• Mixture distribution
• Dirichlet process
• K-means clustering
• Multinomial distribution
• Statistical classification
• Probability distribution
• Mixture
• Statistics
• Machine learning
• Statistical models

Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment (Supplementary Materials) Jason Chuang Stanford University, 353 Serra Mall, Stanford, CA[removed]USA

Source URL: vis.stanford.edu

3 Generalized Multinomial Theorem 3.1 Binomial Theorem Theorem[removed]If x1, x2

Source URL: fractional-calculus.com

• Combinatorics
• Multinomial theorem
• Binomial theorem
• Binomial series
• Mathematics
• Mathematical analysis
• Binomial coefficient

An Extension of the CHAID Tree-based Segmentation Algorithm to Multiple Dependent Variables Jay Magidson1 and Jeroen K. Vermunt2 1 2

Source URL: www.statisticalinnovations.com

• Econometrics
• Categorical data
• Psychometrics
• CHAID
• Latent class model
• Multinomial logit
• Decision tree learning
• Latent variable
• Factor analysis
• Statistics
• Regression analysis
• Decision trees

A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences Eric P. Xing, Michael I. Jordan, Richard M. Karp and Stuart Russell Computer Science Division University of California, Berkeley

Source URL: www.cs.berkeley.edu

• Bayesian statistics
• Probability
• Sequence motif
• Variational Bayesian methods
• Mixture model
• Multinomial distribution
• Dirichlet distribution
• Expectation–maximization algorithm
• Hierarchical Bayes model
• Statistics
• Bioinformatics
• Probability and statistics

Food Quality and Preference[removed]–515 www.elsevier.com/locate/foodqual Factors affecting fluid milk purchasing sources in Turkey q Selim Adem Hatirli a

Source URL: www.econturk.org

• Multinomial logit
• Economics
• Milk
• Raw milk
• Dairy

On the surjectivity of the mapping between utilities and choice probabilities

Source URL: www.economics.illinois.edu

• Multinomial logit
• Economics
• Statistics
• Discrete choice

Colloquium Mixed-membership models of scientific publications Elena Erosheva*†, Stephen Fienberg‡§, and John Lafferty§¶ *Department of Statistics, School of Social Work, and Center for Statistics and the Social S

Source URL: lib.stat.cmu.edu

• Natural language processing
• Probabilistic latent semantic analysis
• Latent Dirichlet allocation
• Multinomial distribution
• Proceedings of the National Academy of Sciences of the United States of America
• Dirichlet distribution
• Latent class model
• Latent semantic analysis
• Information retrieval
• Statistics
• Statistical natural language processing
• Categorical data