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CS 890CQ - Advanced Probabilistic Systems
State-of-the-art models for probabilistic reasoning, including hierarchical Markov networks, nested jointrees, multiply-sectioned Bayesien networks, and the maximal prime decomposition of Bayesian networks. State-of-the-art probability propagation algorithms for inference such as LAZY propagation in the HUGIN expert system 3.000 Credit hours 0.000 TO 3.000 Lecture hours 0.000 TO 3.000 Other hours Levels: Graduate Schedule Types: Lecture, Independent Study, Directed Reading, Examination Computer Science Department Restrictions: Must be enrolled in one of the following Levels: Graduate

CS 890CQ - Advanced Probabilistic Systems

State-of-the-art models for probabilistic reasoning, including hierarchical Markov networks, nested jointrees, multiply-sectioned Bayesien networks, and the maximal prime decomposition of Bayesian networks. State-of-the-art probability propagation algorithms for inference such as LAZY propagation in the HUGIN expert system
3.000 Credit hours
0.000 TO 3.000 Lecture hours
0.000 TO 3.000 Other hours

Levels: Graduate
Schedule Types: Lecture, Independent Study, Directed Reading, Examination

Computer Science Department

Restrictions:
Must be enrolled in one of the following Levels:
Graduate

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Detailed Course Information

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