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Multinomial distribution
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71	Modelling Firm Innovation using Panel Probit Estimators∗ Mark N. Harris Melbourne Institute and Central European University, Hungary. Mark Rogers Harris Manchester College, Oxford University, U.K. Add to Reading List Source URL: www.melbourneinstitute.com Language: English - Date: 2010-04-30 02:37:24 Statistical models Estimation theory Statistical inference Probit model Probit Dummy variable Multinomial probit Maximum likelihood Normal distribution Statistics Econometrics Regression analysis
72	Modelling Firm Innovation using Panel Probit Estimators∗ Mark N. Harris Melbourne Institute and Central European University, Hungary. Mark Rogers Harris Manchester College, Oxford University, U.K. Add to Reading List Source URL: melbourneinstitute.com Language: English - Date: 2010-04-30 02:37:24 Statistical models Estimation theory Statistical inference Probit model Probit Dummy variable Multinomial probit Maximum likelihood Normal distribution Statistics Econometrics Regression analysis
73	Transport Research Arena 2014, Paris A methodology for assessing the impacts of enhanced rail services in South East Europe Iraklis Stamosa, Evangelos Mitsakis a, Josep Maria Salanova Grau a, Georgia Aifadopoulou a a Add to Reading List Source URL: www.traconference.eu Language: English* - Date: 2014-06-17 12:30:53 Economics Mode choice Discrete choice Modal share Trip distribution Mode of transport Choice modelling Multinomial probit Public transport Transportation planning Transport Statistics
74	Clustering Mutational Spectra via Classi 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 Add to Reading List Source URL: eh3.uc.edu Language: English - Date: 2008-06-13 20:29:56 Estimation theory Expectation–maximization algorithm Missing data Cluster analysis Gibbs sampling Normal distribution Multinomial distribution Statistics Multivariate statistics Machine learning
75	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 Add to Reading List Source URL: eh3.uc.edu Language: English - Date: 2008-06-13 20:29:56 Cluster analysis Mixture model Mixture distribution Dirichlet process K-means clustering Multinomial distribution Statistical classification Probability distribution Mixture Statistics Machine learning Statistical models
76	Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment (Supplementary Materials) Jason Chuang Stanford University, 353 Serra Mall, Stanford, CA[removed]USA Add to Reading List Source URL: vis.stanford.edu Language: English - Date: 2013-05-14 19:33:10
77	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 Add to Reading List Source URL: www.cs.berkeley.edu Language: English - Date: 2005-10-27 20:39:29 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
78	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 Add to Reading List Source URL: lib.stat.cmu.edu Language: English - Date: 2004-07-28 12:15:38 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
79	Spatial Latent Dirichlet Allocation Xiaogang Wang and Eric Grimson Add to Reading List Source URL: machinelearning.wustl.edu Language: English - Date: 2013-03-22 13:03:50 Science Latent Dirichlet allocation Dynamic topic model Topic model Search engine indexing Multinomial distribution Language model Bag of words model in computer vision Statistical natural language processing Statistics Information science
80	ANALYSIS OF A BOSE-EINSTEIN MARKOV CHAIN Persi Diaconis Department of Mathematics and Statistics Add to Reading List Source URL: www-stat.stanford.edu Language: English - Date: 2004-02-04 17:32:57 Markov models Markov processes Markov chain Bose–Einstein statistics Pólya enumeration theorem Multinomial distribution Prime-counting function Permutation Stochastic matrix Mathematics Combinatorics Statistics