A structure for representing problems in decision analysis and in expert systems, which reason under uncertainty, is the influence diagram or causal network. A causal network consists of an underlying joint probability distribution and a directed acyclic graph in which a propositional variable that represents a marginal distribution is stored at each vertex in the graph. This paper is concerned with two of the problems in applications that use causal networks. The first problem is the determination of the conditional probabilities of the values of remaining propositional variables in the network given that certain variables are instantiated for particular values. This is called probability propagation. The second problem is the determination of the most probable, second most probable, third most probable, and so on sets of values of a particular set of variables (called the explanation set) given that certain variables are instantiated for particular values. This problem is called abductive inference. There exists a class of causal networks in which each variable has only two parents, for which the time required, by any known method, for probability propagation is exponential relative to the number of vertices in the network. The determination of a new method that would be efficient for all causal networks appears unlikely, because probability propagation has been shown to be #P-complete. In many medical applications, networks are often large and not sparsely connected. Therefore a method for the exact determination of probability values appears unlikely for such applications, and the development of approximation methods seems to be the best solution. The current approximation methods obtain interval bounds for the probability values. When such intervals are obtained, it is not possible in general to rank the alternatives. In this paper, a method is developed for obtaining expected values for the point probabilities from interval constraints on the probabilities. The method is based on an application of the principle of indifference to the probability values themselves. The distributions obtained with the principle of indifference are a generalization of the symmetric Dirichlet distribution in which prior ignorance is assumed.
ASJC Scopus subject areas
- Statistics and Probability
- Modeling and Simulation
- Biochemistry, Genetics and Molecular Biology(all)
- Immunology and Microbiology(all)
- Agricultural and Biological Sciences(all)
- Applied Mathematics