Weighted positive binary decision diagrams for exact probabilistic inference
| Autor: | Peter J. F. Lucas, Giso H. Dal |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Logic in Computer Science Theoretical computer science Computer Science - Artificial Intelligence Binary decision diagram Applied Mathematics Bayesian network 02 engineering and technology Logic in Computer Science (cs.LO) Theoretical Computer Science Artificial Intelligence (cs.AI) Artificial Intelligence Frequentist inference 020204 information systems Satisfiability modulo theories ComputingMethodologies_DOCUMENTANDTEXTPROCESSING 0202 electrical engineering electronic engineering information engineering Fiducial inference Software Science Probability distribution Influence diagram 020201 artificial intelligence & image processing Probabilistic relevance model Software Mathematics |
| Zdroj: | International Journal of Approximate Reasoning, 90, Supplement C, pp. 411-432 International Journal of Approximate Reasoning, 90, 411-432 International Journal of Approximate Reasoning, 90, 411-443 |
| ISSN: | 0888-613X |
| DOI: | 10.1016/j.ijar.2017.08.003 |
| Popis: | Recent work on weighted model counting has been very successfully applied to the problem of probabilistic inference in Bayesian networks. The probability distribution is encoded into a Boolean normal form and compiled to a target language, in order to represent local structure expressed among conditional probabilities more efficiently. We show that further improvements are possible, by exploiting the knowledge that is lost during the encoding phase and incorporating it into a compiler inspired by Satisfiability Modulo Theories. Constraints among variables are used as a background theory, which allows us to optimize the Shannon decomposition. We propose a new language, called Weighted Positive Binary Decision Diagrams, that reduces the cost of probabilistic inference by using this decomposition variant to induce an arithmetic circuit of reduced size. 30 pages |
| Databáze: | OpenAIRE |
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