Showing papers by "David Poole published in 1990"
01 Jan 1990
TL;DR: This work describes a non-numeric formalism called an inference graph based on standard probability theory, conditional independence and sentences of favouring where a favours b ≡ favours(a, b) ≡ p( a\b) p(a).
Abstract: There is much interest in providing probabilistic semantics for defaults but most approaches seem to suffer from one of two problems: either they require numbers, a problem defaults were intended to avoid, or they generate peculiar side effects. Rather than provide semantics for defaults, we address the problem defaults were intended to solve: that of reasoning under uncertainty where numeric probability distributions are not available. We describe a non-numeric formalism called an inference graph based on standard probability theory, conditional independence and sentences of favouring where a favours b ≡ favours(a, b) ≡ p(a\b) p(a). The formalism seems to handle the examples from the nonmonotonic literature. Most importantly, the sentences of our system can be verified by performing an appropriate experiment in the semantic domain.
23 citations
01 Jan 1990
TL;DR: In this paper, the feasibility of using finite totally ordered probability models under Aleliunas's theory of probabilistic logic is investigated and the general form of the probability algebra of these models is derived and the number of possible algebras with given size is deduced.
Abstract: In this paper, the feasibility of using finite totally ordered probability models under Aleliunas's Theory of Probabilistic Logic [Aleliunas, 1988] is investigated. The general form of the probability algebra of these models is derived and the number of possible algebras with given size is deduced. Based on this analysis, we discuss problems of denominator-indifference and ambiguity-generation that arise in reasoning by cases and abductive reasoning. An example is given that illustrates how these problems arise. The investigation shows that a finite probability model may be of very limited usage.
13 citations
01 Jul 1990
TL;DR: A new algorithm (ALPP) is presented that allows refinement of FCPs based on expert estimates of posterior probability and applies to any DAG of diameter 1.
Abstract: The Bayesian network is a powerful knowledge representation formalism; it is also capable of improving its precision through experience. Spiegelhalter et al. [1989] proposed a procedure for sequential updating forward conditional probabilities (FCP) in Bayesian networks of diameter 1 with a single parent node. The procedure assumes certainty for each diagnosis which is not practical for many applications. In this paper we present a new algorithm (ALPP) that allows refinement of FCPs based on expert estimates of posterior probability. ALPP applies to any DAG of diameter 1. Fast convergence is achieved. Simulation results compare ALPP with Spiegelhalter’s method.
5 citations