Abstract
This paper addresses the problem of learning structure of Bayesian and Dynamic Bayesian networks from incomplete data based on the Bayesian Information Criterion. We describe a procedure to map the problem of the dynamic case into a corresponding augmented Bayesian network through the use of structural constraints. Because the algorithm is exact and anytime, it is well suitable for a structural Expectation-Maximization (EM) method where the only source of approximation is due to the EM itself. We show empirically that the use a global maximizer inside the structural EM is computationally feasible and leads to more accurate models.
Original language | English |
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Title of host publication | Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 601-604 |
Number of pages | 4 |
ISBN (Electronic) | 978-1-4244-7541-4 |
ISBN (Print) | 978-1-4244-7542-1 |
DOIs | |
Publication status | Published - 18 Nov 2010 |
Externally published | Yes |
Event | 2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey Duration: 23 Aug 2010 → 26 Aug 2010 |
Conference
Conference | 2010 20th International Conference on Pattern Recognition, ICPR 2010 |
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Country/Territory | Turkey |
City | Istanbul |
Period | 23/08/10 → 26/08/10 |