J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. ...  Home/Search
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Abstract: this article, we find that most of the factors are very close to # while four of the factors achieve significant expected counts. Looking at the distribution over words, z), for those four factors, we can identify the topics which mixed via the # random variable to form this document (Figure 2) (Update)
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BibTeX entry: (Update)
@misc{ berger-bayesian,
author = "Bernardo Bayarri Berger",
title = "BAYESIAN STATISTICS 7, pp. 25--43",
url = "citeseer.ist.psu.edu/757014.html" }
Citations (may not include all citations):372 Modern Information Retrieval - Baeza-Yates, Ribeiro-Neto - 1999
245 Introduction to variational methods for graphical models - Jordan, Ghahramani et al. - 1999
202 Statistical Methods for Speech Recognition (context) - Jelinek - 1997
164 Latent Dirichlet allocation - Blei, Jordan et al. - 2002
123 Probabilistic latent semantic indexing - Hofmann - 1999
120 A variational Bayesian framework for graphical models - Attias - 2000
49 Overview of the first text retrieval conference (context) - Harman - 1992
41 Using maximum entropy for text classification - Nigam, Lafferty et al. - 1999
17 Document language models (context) - Zhai, Lafferty - 2001
15 Modeling annotated data - Blei, Jordan - 2002
12 Modelling heterogeneity with and without the Dirichlet proce.. - Soc, -- et al. - 2001
3 Multiple hypergeometric functions: Probabilistic interpretat.. (context) - Dickey - 1983
2 Document clustering: An evaluation of some experiments with .. (context) - van Rijsbergen, Croft - 1975
1 Estimation of finite mixture distributions through Bayesian .. (context) - Assoc, Diebolt et al. - 1994
1 Bayesian methods for censored categorical data (context) - Assoc, Dickey et al. - 1987
1 San Mateo: CA (context) - eds - 1989
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