Bayesian probability

Results: 1640



#Item
51The Method of Simulated Quantiles Yves Dominicy∗ and David Veredas †  First draft: DecemberThis version: May 2010

The Method of Simulated Quantiles Yves Dominicy∗ and David Veredas † First draft: DecemberThis version: May 2010

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Source URL: www.istfin.eco.usi.ch

Language: English - Date: 2010-10-18 11:21:40
52Chapter 5  Statistical inference Probability and statistics can be considered one as the inverse problem of the other. In the previous chapters we studied what is the theory of probability. Given a probability density fu

Chapter 5 Statistical inference Probability and statistics can be considered one as the inverse problem of the other. In the previous chapters we studied what is the theory of probability. Given a probability density fu

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Source URL: ihp-lx.ethz.ch

Language: English - Date: 2015-03-31 05:26:54
53Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation Dahua Lin Toyota Technological Institute at Chicago

Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation Dahua Lin Toyota Technological Institute at Chicago

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Source URL: dahua.me

Language: English - Date: 2013-10-22 19:40:40
54Technical Note Series  Spring 2013 Note 1: Varitional Methods for Latent Dirichlet Allocation Version 1.0

Technical Note Series Spring 2013 Note 1: Varitional Methods for Latent Dirichlet Allocation Version 1.0

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Source URL: playbigdata.com

Language: English - Date: 2014-07-16 22:13:04
55A Rothschild-Stiglitz approach to Bayesian persuasion Matthew Gentzkow and Emir Kamenica∗ Stanford University and University of Chicago JanuaryConsider a situation where one person, call him Sender, generates in

A Rothschild-Stiglitz approach to Bayesian persuasion Matthew Gentzkow and Emir Kamenica∗ Stanford University and University of Chicago JanuaryConsider a situation where one person, call him Sender, generates in

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Source URL: faculty.chicagobooth.edu

Language: English - Date: 2016-01-14 19:35:40
56Learning Latent Groups with Hinge-loss Markov Random Fields  Stephen H. Bach Bert Huang Lise Getoor University of Maryland, College Park, Maryland 20742, USA

Learning Latent Groups with Hinge-loss Markov Random Fields Stephen H. Bach Bert Huang Lise Getoor University of Maryland, College Park, Maryland 20742, USA

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Source URL: stephenbach.net

Language: English - Date: 2013-06-14 15:37:03
57Lina Eriksson ´jek Alan Ha Abstract.

Lina Eriksson ´jek Alan Ha Abstract.

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Source URL: dl.dropboxusercontent.com

Language: English
58Approximate Bayesian Computation for Granular and Molecular Dynamics Simulations Lina Kulakova Computational Science and Engineering Laboratory ETH Zürich

Approximate Bayesian Computation for Granular and Molecular Dynamics Simulations Lina Kulakova Computational Science and Engineering Laboratory ETH Zürich

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Source URL: www.cse-lab.ethz.ch

Language: English - Date: 2016-06-15 20:10:57
59Big DataConference Program Monday, August 22 9:00-9:50am Yiling Chen, Harvard University Title: “Machine Learning with Strategic Data Sources”

Big DataConference Program Monday, August 22 9:00-9:50am Yiling Chen, Harvard University Title: “Machine Learning with Strategic Data Sources”

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Source URL: cmsa.fas.harvard.edu

Language: English - Date: 2016-08-03 13:09:29
60RMM Vol. 2, 2011, 103–114 Special Topic: Statistical Science and Philosophy of Science Edited by Deborah G. Mayo, Aris Spanos and Kent W. Staley http://www.rmm-journal.de/  Sir David Cox and Deborah Mayo

RMM Vol. 2, 2011, 103–114 Special Topic: Statistical Science and Philosophy of Science Edited by Deborah G. Mayo, Aris Spanos and Kent W. Staley http://www.rmm-journal.de/ Sir David Cox and Deborah Mayo

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Source URL: www.rmm-journal.com

Language: English - Date: 2011-10-18 07:00:37