http://www.bibsonomy.org/burst/user/schmitz/statisticsBibSonomy BuRST Feed for /user/schmitz/statistics2008-07-21T01:38:44+02:00DBLP Record 'conf/www/ZhouMLGZ06'http://www.bibsonomy.org/bibtex/20de5ddee799f24fff431d616bd309258/schmitzschmitz2006-11-09T19:30:55+01:00statistics community <span style="color:#555555;">Ding <a href="http://www.bibsonomy.org/author/Zhou">Zhou</a> and Eren <a href="http://www.bibsonomy.org/author/Manavoglu">Manavoglu</a> and Jia <a href="http://www.bibsonomy.org/author/Li">Li</a> and C. <a href="http://www.bibsonomy.org/author/Lee Giles">Lee Giles</a> and Hongyuan <a href="http://www.bibsonomy.org/author/Zha">Zha</a> </span><em>WWW, </em><em>page173-182. </em><em>ACM, </em>(<em>2006</em>)Thu Nov 09 19:30:55 CET 2006WWWDBLP:conf/www/2006173-182Probabilistic models for discovering e-communities.2006statistics community A Brief History of Generative Models for Power Law and Lognormal Distributions - Mitzenmacher (ResearchIndex)http://www.bibsonomy.org/bibtex/21acf355dc265826132ea7d7f41085bee/schmitzschmitz2006-09-26T13:31:27+02:00statistics powerlaw distribution <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Mitzenmacher">Mitzenmacher</a> </span>(<em>year unknown</em>)Tue Sep 26 13:31:27 CEST 2006A brief history of generative models for power law and lognormal distributionsyear unknownstatistics powerlaw distribution http://www.bibsonomy.org/bibtex/216802fb465ca95ac77434bf73c6b271b/schmitzschmitz2006-09-26T09:25:16+02:00generative modeling statistics <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Steyvers">Steyvers</a> and T. <a href="http://www.bibsonomy.org/author/Griffiths">Griffiths</a> </span><em>Latent Semantic Analysis: A Road to Meaning, </em><em>Laurence Erlbaum, </em>(<em>2005</em>)Tue Sep 26 09:25:16 CEST 2006Latent Semantic Analysis: A Road to MeaningProbabilistic topic models2005generative modeling statistics Many chapters in this book illustrate that applying a statistical method such as Latent Semantic Analysis (LSA; Landauer \& Dumais, 1997; Landauer, Foltz, \& Laham, 1998) to large databases can yield insight into human cognition. The LSA approach makes three claims: that semantic information can be derived from a word-document co-occurrence matrix; that dimensionality reduction is an essential part of this derivation; and that words and documents can be represented as points in Euclidean space. In this chapter, we pursue an approach that is consistent with the first two of these claims, but differs in the third, describing a class of statistical models in which the semantic properties of words and documents are expressed in terms of probabilistic topics.http://www.bibsonomy.org/bibtex/2a4dd688efe5778fb99ff94de104211aa/schmitzschmitz2006-09-26T09:25:07+02:00statistics generative modeling <span style="color:#555555;">Michal <a href="http://www.bibsonomy.org/author/Rosen-Zvi">Rosen-Zvi</a> and Thomas <a href="http://www.bibsonomy.org/author/Griffiths">Griffiths</a> and Mark <a href="http://www.bibsonomy.org/author/Steyvers">Steyvers</a> and Padhraic <a href="http://www.bibsonomy.org/author/Smyth">Smyth</a> </span><em>Proceedings of the 20th conference on Uncertainty in artificial intelligence, </em><em>page487--494. </em><em>Arlington, VA, USA, </em><em>AUAI Press, </em>(<em>2004</em>)Tue Sep 26 09:25:07 CEST 2006Arlington, VA, USAProceedings of the 20th conference on Uncertainty in artificial intelligence487--494The author-topic model for authors and documents2004statistics generative modeling http://www.bibsonomy.org/bibtex/24a5138f8d572d2f89e2b94ec60986278/schmitzschmitz2006-09-26T09:25:01+02:00generative modeling statistics <span style="color:#555555;">A. <a href="http://www.bibsonomy.org/author/Mccallum">Mccallum</a> and A. <a href="http://www.bibsonomy.org/author/Corrada-Emmanuel">Corrada-Emmanuel</a> and X. <a href="http://www.bibsonomy.org/author/Wang">Wang</a> </span><em>ijcai.org, </em>(<em>2005</em>)Tue Sep 26 09:25:01 CEST 2006ijcai.orgTopic and role discovery in social networks2005generative modeling statistics Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the language content or topics on those links. We present the Author- Recipient-Topic (ART) model for social network analysis, which learns topic distributions based on the direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) and the Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly on both the sender and recipient—steering the discovery of topics according to the relationships between people. We give results on both the Enron email corpus and a researcher’s email archive, providing evidence not only that clearly relevant topics are discovered, but that the ART model better predicts people’s roles.Operations for Learning with Graphical Models - Buntine (ResearchIndex)http://www.bibsonomy.org/bibtex/28952cf0d215116e038971f7c30d6d19d/schmitzschmitz2006-09-21T10:29:54+02:00kdd statistics graphicalmodel buntine ml researcher <span style="color:#555555;">Wray L. <a href="http://www.bibsonomy.org/author/Buntine">Buntine</a> </span><em>Journal of Artificial Intelligence Research</em>(<em>1994</em>)Thu Sep 21 10:29:54 CEST 2006Journal of Artificial Intelligence Research159-225Operations for Learning with Graphical Models21994kdd statistics graphicalmodel buntine ml researcher http://www.bibsonomy.org/bibtex/22eb09a4cf947471c5a11ef9e1319d4ec/schmitzschmitz2006-09-12T18:51:38+02:00graph statistics graphgenerator evolution <span style="color:#555555;">Matti <a href="http://www.bibsonomy.org/author/Peltomäki">Peltom&#228;ki</a> and Mikko <a href="http://www.bibsonomy.org/author/Alava">Alava</a> </span><em>J.STAT.MECH.</em>(<em>2006</em>)Tue Sep 12 18:51:38 CEST 2006J.STAT.MECH.P01010Correlations in Bipartite Collaboration Networks2006graph statistics graphgenerator evolution