MCE Reasoning in Recursive Causal Networks
A probabilistic method of reasoning under uncertainty is proposed based on the principle of Minimum Cross Entropy (MCE) and concept of Recursive Causal Model (RCM). The dependency and correlations among the variables are described in a special language BNDL (Belief Networks Description Language). Be...
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| creator | Wen, Wilson X |
| description | A probabilistic method of reasoning under uncertainty is proposed based on
the principle of Minimum Cross Entropy (MCE) and concept of Recursive Causal
Model (RCM). The dependency and correlations among the variables are described
in a special language BNDL (Belief Networks Description Language). Beliefs are
propagated among the clauses of the BNDL programs representing the underlying
probabilistic distributions. BNDL interpreters in both Prolog and C has been
developed and the performance of the method is compared with those of the
others. |
| doi_str_mv | 10.48550/arxiv.1304.2380 |
| format | Article |
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the principle of Minimum Cross Entropy (MCE) and concept of Recursive Causal
Model (RCM). The dependency and correlations among the variables are described
in a special language BNDL (Belief Networks Description Language). Beliefs are
propagated among the clauses of the BNDL programs representing the underlying
probabilistic distributions. BNDL interpreters in both Prolog and C has been
developed and the performance of the method is compared with those of the
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the principle of Minimum Cross Entropy (MCE) and concept of Recursive Causal
Model (RCM). The dependency and correlations among the variables are described
in a special language BNDL (Belief Networks Description Language). Beliefs are
propagated among the clauses of the BNDL programs representing the underlying
probabilistic distributions. BNDL interpreters in both Prolog and C has been
developed and the performance of the method is compared with those of the
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the principle of Minimum Cross Entropy (MCE) and concept of Recursive Causal
Model (RCM). The dependency and correlations among the variables are described
in a special language BNDL (Belief Networks Description Language). Beliefs are
propagated among the clauses of the BNDL programs representing the underlying
probabilistic distributions. BNDL interpreters in both Prolog and C has been
developed and the performance of the method is compared with those of the
others.</abstract><doi>10.48550/arxiv.1304.2380</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence |
| title | MCE Reasoning in Recursive Causal Networks |
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