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Bayesian programming
Results: 114

#	Item
91	Nonparametric Bayesian Models for Unsupervised Scene Analysis and Reconstruction Dominik Joho, Gian Diego Tipaldi, Nikolas Engelhard, Cyrill Stachniss, Wolfram Burgard Department of Computer Science, University of Freibu Add to Reading List Source URL: www.first-mm.eu Language: English - Date: 2012-08-20 05:33:12 Object-oriented programming Unified Modeling Language 3D computer graphics Object composition Constellation model Dirichlet process Rendering Ada Computer vision Software engineering Computing Computer programming
92	Nonparametric Bayesian Models for Unsupervised Scene Analysis and Reconstruction Dominik Joho, Gian Diego Tipaldi, Nikolas Engelhard, Cyrill Stachniss, Wolfram Burgard Department of Computer Science, University of Freibu Add to Reading List Source URL: first-mm.informatik.uni-freiburg.de Language: English - Date: 2012-08-20 05:33:12 Object-oriented programming Unified Modeling Language 3D computer graphics Object composition Constellation model Dirichlet process Rendering Ada Computer vision Software engineering Computing Computer programming
93	BaDGE (Bayesian model for Detecting Gene Environment interaction) August 16, 2011 > library(BaDGE) Add to Reading List Source URL: dceg.cancer.gov Language: English - Date: 2013-05-14 07:29:32 Lookup table Software optimization Computer programming Computing Arrays Computer performance
94	Abstract It has been shown that if two probability distributions satisfy the monotone likelihood ratio property (MLRP), and are independently updated using common public information and traditional Bayesian updating, the Add to Reading List Source URL: ben.klemens.org Language: English - Date: 2008-01-23 16:18:24 Order theory Hypothesis testing Monotone likelihood ratio Prior probability Feature-oriented programming Continuous function Convex function Monotonic function Single crossing condition Mathematical analysis Bayesian statistics Programming paradigms
95	Journal of Machine Learning Research[removed]Submitted 11/10; Published - Deterministic-Probabilistic Models For Partially Observable Reinforcement Learning Problems Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2010-11-25 21:48:41 Stochastic control Partially observable Markov decision process Reinforcement learning Bayesian statistics Markov model Markov decision process Automated planning and scheduling Algorithm Machine learning Statistics Dynamic programming Markov processes
96	Bayesian Optimal Active Search and Surveying Roman Garnett [removed] Yamuna Krishnamurthy [removed] Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2013-02-04 21:29:26 Measure theory Decision theory Operations research Systems engineering Dynamic programming Markov decision process Dirac delta function Optimal decision Support Mathematical analysis Statistics Control theory
97	G:AcadsAutonialarmgraphsother formatspsdistr.eps Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2005-01-21 17:02:57 Partially observable Markov decision process Stochastic control Bayesian statistics CUSUM Maximum likelihood Prior probability Normal distribution Thresholding Statistics Estimation theory Dynamic programming
98	Detecting Anomalous Records in Categorical Datasets Kaustav Das Machine Learning Department Carnegie Mellon University Pittsburgh PA 15203 Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2007-06-26 14:42:17 Probability and statistics Data mining Programming paradigms Anomaly detection Data security Supervised learning Bayesian network Mixture model Attribute grammar Statistics Machine learning Statistical models
99	Package ‘ggmcmc’ July 2, 2014 Title Graphical tools for analyzing Markov Chain Monte Carlo simulations from Bayesian inference. Description ggmcmc is a tool for assessing and diagnosing convergence of Markov Chain Mo Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-07-02 11:59:07 Subroutines Environmental science Parameter Test Markov chain Bracket Constructor Null C Software engineering Computing Computer programming
100	Diagnose and Decide: An Optimal Bayesian Approach Emma Brunskill Computer Science Department Carnegie Mellon University [removed] Add to Reading List Source URL: lis.csail.mit.edu Language: English - Date: 2013-03-11 16:09:52 Stochastic control Decision theory Partially observable Markov decision process Markov decision process Optimal decision Statistical hypothesis testing Algorithm Greedy algorithm Decision making Statistics Dynamic programming Markov processes

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