Markov logic network

Results: 70



#Item
11Anytime Inference in Probabilistic Logic Programs with TP -Compilation Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt Department of Computer Science KU Leuven, Belgium firstname.lastnam

Anytime Inference in Probabilistic Logic Programs with TP -Compilation Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt Department of Computer Science KU Leuven, Belgium firstname.lastnam

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Source URL: lirias.kuleuven.be

Language: English - Date: 2015-11-20 04:37:18
12arXiv:1507.02456v1  [cs.AI]  9 Jul 2015

arXiv:1507.02456v1 [cs.AI] 9 Jul 2015

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Source URL: arxiv.org

Language: English - Date: 2015-07-09 20:33:34
13Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic William Yang Wang Kathryn Mazaitis

Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic William Yang Wang Kathryn Mazaitis

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Source URL: www.cs.cmu.edu

Language: English - Date: 2013-08-11 21:49:35
141 Logic-based Formalisms for Statistical Relational Learning James Cussens Department of Computer Science & York Centre for Complex Systems Analysis

1 Logic-based Formalisms for Statistical Relational Learning James Cussens Department of Computer Science & York Centre for Complex Systems Analysis

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Source URL: www.cs.york.ac.uk

Language: English - Date: 2008-04-29 12:08:04
15Theoretical computer science / Bayesian statistics / Networks / Probability and statistics / Applied mathematics / Bayesian network / Probabilistic logic / Algorithm / Decomposition method / Statistics / Graphical models / Statistical models

Problem Solving in Graphical models Rina Dechter, Donald Bren School of Information and Computer Science, UC-Irvine Graphical models, including constraint networks, belief networks, Markov random fields and influence

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Source URL: www.ics.uci.edu

Language: English - Date: 2012-12-28 02:42:27
16Sampling Techniques for Probabilistic and Deterministic Graphical models

Sampling Techniques for Probabilistic and Deterministic Graphical models

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Source URL: www.ics.uci.edu

Language: English - Date: 2014-11-19 16:19:36
17Sampling Techniques for Probabilistic and Deterministic Graphical models Bozhena Bidyuk Vibhav Gogate Rina Dechter

Sampling Techniques for Probabilistic and Deterministic Graphical models Bozhena Bidyuk Vibhav Gogate Rina Dechter

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Source URL: www.ics.uci.edu

Language: English - Date: 2010-07-20 05:28:43
18A Characterization of Interventional Distributions in Semi-Markovian Causal Models Jin Tian and Changsung Kang Department of Computer Science Iowa State University Ames, IA 50011

A Characterization of Interventional Distributions in Semi-Markovian Causal Models Jin Tian and Changsung Kang Department of Computer Science Iowa State University Ames, IA 50011

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Source URL: www.cs.iastate.edu

Language: English - Date: 2006-04-25 20:04:35
19Evidence-Based Clustering for Scalable Inference in Markov Logic Deepak Venugopal and Vibhav Gogate Computer Science Department The University of Texas at Dallas Richardson, USA

Evidence-Based Clustering for Scalable Inference in Markov Logic Deepak Venugopal and Vibhav Gogate Computer Science Department The University of Texas at Dallas Richardson, USA

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Source URL: www.hlt.utdallas.edu

Language: English - Date: 2014-06-28 05:36:54
20Lifted MAP Inference for Markov Logic Networks  Somdeb Sarkhel, Deepak Venugopal Department of Computer Science The University of Texas at Dallas Richardson, TX 75080, USA

Lifted MAP Inference for Markov Logic Networks Somdeb Sarkhel, Deepak Venugopal Department of Computer Science The University of Texas at Dallas Richardson, TX 75080, USA

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Source URL: www.hlt.utdallas.edu

Language: English - Date: 2014-02-25 21:05:55