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Multiple types of inference are available for probabilistic graphical models, e.g., marginal, maximum-a-posteriori, and even marginal maximum-a-posteriori.
“An introduction to variational methods for graphical models”
Michael Jordan, Zoubin Ghahramani, Tommi Jaakkola and Lawrence Saul · 1999
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Jonathan Yedidia, William Freeman and Yair Weiss · 2005
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Marc Toussaint and Amos Storkey · 2006
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Ross Shachter · 2007
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“Probabilistic planning via determinization in hindsight.”
Sung Yoon, Alan Fern, Robert Givan and Subbarao Kambhampati · 2008
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“Norm-product belief propagation: Primal-dual message-passing for approximate inference”
Tamir Hazan and Amnon Shashua · 2010
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Hans F\"ollmer and Alexander Schied · 2011
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Scott Sanner · 2011
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“Introduction to dual decomposition for inference”, 2011
David Sontag, Amir Globerson and Tommi Jaakkola · 2011
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Judea Pearl · 2012
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Yair Weiss, Chen Yanover and Talya Meltzer · 2012
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Qiang Cheng, Qiang Liu, Feng Chen and Alexander Ihler · 2013
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Qiang Liu and Alexander Ihler · 2013
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“Applying marginal map search to probabilistic conformant planning: Initial results”
Junkyu Lee, Radu Marinescu and Rina Dechter · 2014
“Applying Search Based Probabilistic Inference Algorithms to Probabilistic Conformant Planning: Preliminary Results.”
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Arnab Bhattacharya and Jeffrey Kharoufeh · 2017
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Paul Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris Mooij, Dominik Janzing, Moritz Grosse-Wentrup and Bernhard Sch\"olkopf · 2017
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“From stochastic planning to marginal MAP”
Hao Cui, Radu Marinescu and Roni Khardon · 2018
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“Join Graph Decomposition Bounds for Influence Diagrams.”
Junkyu Lee, Alexander Ihler and Rina Dechter · 2018
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