Recursive noisy OR- a rule for estimating complex probabilistic interactions Export

@article{lemmer2004rno, abstract = {This paper focuses on approaches that address the intractability of knowledge acquisition of conditional probability tables in causal or Bayesian belief networks. We state a rule that we term the ``recursive noisy OR'' (RNOR) which allows combinations of dependent causes to be entered and later used for estimating the probability of an effect. In the development of this paper, we investigate the axiomatic correctness and semantic meaning of this rule and show that the recursive noisy OR is a generalization of the wellknown noisy OR. We introduce the concept of positive causality and demonstrate its utility in axiomatic correctness of the RNOR. We also introduce concepts describing the ways in which dependent causes can work together as being either ``synergistic'' or ``interfering.'' We provide a formalization to quantify these concepts and show that they are preserved by the RNOR. Finally, we present a method for the determination of Conditional Probability Tables from this causal theory.}, author = {Lemmer, J. F. and Gossink, D. E.}, citeulike-article-id = {2916376}, journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics}, keywords = {causality}, number = {6}, pages = {2252--2261}, posted-at = {2008-06-23 07:12:42}, priority = {2}, title = {Recursive noisy OR- a rule for estimating complex probabilistic interactions}, volume = {34}, year = {2004} }

TY - JOUR ID - lemmer2004rno L3 - citeulike-article-id:2916376 N2 - This paper focuses on approaches that address the intractability of knowledge acquisition of conditional probability tables in causal or Bayesian belief networks. We state a rule that we term the ``recursive noisy OR'' (RNOR) which allows combinations of dependent causes to be entered and later used for estimating the probability of an effect. In the development of this paper, we investigate the axiomatic correctness and semantic meaning of this rule and show that the recursive noisy OR is a generalization of the wellknown noisy OR. We introduce the concept of positive causality and demonstrate its utility in axiomatic correctness of the RNOR. We also introduce concepts describing the ways in which dependent causes can work together as being either ``synergistic'' or ``interfering.'' We provide a formalization to quantify these concepts and show that they are preserved by the RNOR. Finally, we present a method for the determination of Conditional Probability Tables from this causal theory. IS - 6 JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics EP - 2261 TI - Recursive noisy OR- a rule for estimating complex probabilistic interactions VL - 34 SP - 2252 KW - causality AU - Lemmer, JF AU - Gossink, DE PY - 2004/// ER -