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Bayesian programming
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81	1 Bayesian Logic Programming: Theory and Tool Kristian Kersting Institute for Computer Science, Machine Learning Lab Albert-Ludwigs-Universit¨ Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2007-02-02 14:16:03 Graphical models Networks Philosophy of science Probability and statistics Science Bayesian network Markov random field Bayesian probability Conditional independence Bayesian statistics Statistics Probability theory
82	Decentralized Bayesian Reinforcement Learning for Online Agent Collaboration A. Rogers1 , N. R. Jennings1 S. McClean4 , G. Parr4 W. T. L. Teacy1 , G. Chalkiadakis2 Add to Reading List Source URL: eprints.soton.ac.uk Language: English - Date: 2012-06-06 11:47:06 Bayesian statistics Stochastic control Markov models Markov decision process Reinforcement learning Conjugate prior Bayesian inference Q-learning Action selection Statistics Dynamic programming Markov processes
83	Structure Learning of Markov Logic Networks through Iterated Local Search Marenglen Biba and Stefano Ferilli and Floriana Esposito1 Abstract. Many real-world applications of AI require both probability and first-order lo Add to Reading List Source URL: www.comp.leeds.ac.uk Language: English - Date: 2008-08-18 09:29:21 Logic Automated theorem proving Theoretical computer science Mathematical logic Statistical models Markov logic network Algorithm Logic programming Resolution Bayesian statistics Statistics Mathematics
84	Selecting Strategies for Infinite-Horizon Dynamic LIMIDs Marcel A.J. van Gerven Institute for Computing and Information Sciences Radboud University Nijmegen Toernooiveld[removed]ED Nijmegen, The Netherlands Add to Reading List Source URL: www.utia.cas.cz Language: English - Date: 2008-12-17 13:50:27 Mathematical optimization Equations Dynamic programming Operations research Optimal control Markov decision process Bayesian network Variable Bellman equation Statistics Mathematics Control theory
85	Exploiting Fully Observable and Deterministic Structures in Goal POMDPs Add to Reading List Source URL: www.ida.liu.se Language: English - Date: 2013-08-29 10:13:42 Stochastic control Partially observable Markov decision process Markov decision process Automated planning and scheduling Bayesian network FO S0 Finite-state machine Macro Statistics Dynamic programming Markov processes
86	Structure Learning of Markov Logic Networks through Iterated Local Search Marenglen Biba and Stefano Ferilli and Floriana Esposito1 Abstract. Many real-world applications of AI require both probability and first-order lo Add to Reading List Source URL: www.di.uniba.it Language: English - Date: 2008-10-06 05:35:01 Logic Automated theorem proving Theoretical computer science Mathematical logic Statistical models Markov logic network Algorithm Logic programming Resolution Bayesian statistics Statistics Mathematics
87	Structure Learning of Markov Logic Networks through Iterated Local Search Marenglen Biba and Stefano Ferilli and Floriana Esposito1 Abstract. Many real-world applications of AI require both probability and first-order lo Add to Reading List Source URL: www.di.uniba.it Language: English - Date: 2008-10-06 05:35:01 Logic Automated theorem proving Theoretical computer science Mathematical logic Statistical models Markov logic network Algorithm Logic programming Resolution Bayesian statistics Statistics Mathematics
88	Available online at www.sciencedirect.com Computer Speech and Language[removed]–174 COMPUTER SPEECH AND Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2010-05-02 07:09:58 Stochastic control Partially observable Markov decision process Graphical models Probability theory Dialogue Probability Bayesian network Markov decision process Markov chain Statistics Markov processes Dynamic programming
89	Probabilistic Programming Andrew D. Gordon Thomas A. Henzinger Aditya V. Nori Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2014-02-03 11:01:04 Probability and statistics Markov models Non-classical logic Probabilistic logic Markov chain Bayesian network Bernoulli distribution Graphical model Probability distribution Statistics Bayesian statistics Statistical models
90	Conditional Independence Lecture 2: Directed Graphical Models • Notation: xA ⊥ xB \|xC Definition: two (sets of) variables xA and xB are conditionally Add to Reading List Source URL: www.cs.nyu.edu Language: English - Date: 2009-10-06 18:57:12 Probability theory Probability and statistics Graphical model Independence Joint probability distribution Probability Constraint programming Maths24 COMPASS/Sample Code Statistics Bayesian statistics Statistical models

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