This paper proposes a new algorithm for recovery of belief network structure from data handling hidden variables. It consists essentially in an extension of the CI algorithm of Spirtes et al. by restricting the number of conditional dependencies checked up to k variables and in an extension of the original CI by additional steps transforming so called partial including path graph into a belief ...

Belief propagation (BP) is the calculation method which enables us to obtain the marginal probabilities with a tractable computational cost. BP is known to provide true marginal probabilities when the graph describing the target distribution has a tree structure, while do approximate marginal probabilities when the graph has loops. The accuracy of loopy belief propagation (LBP) has been studied...

This paper describes a novel and competitive complete conformant planner. Key to the enhanced performance is an efficient encoding of belief states as disjunctive normal form formulae and an efficient procedure for computing the successor belief state. We provide experimental comparative evaluation on a large pool of benchmarks. The novel design provides great efficiency and enhanced scalabilit...

In a teaching and learning environment Bayesian network fits well because it can adjust its structure as per data presented to it. When a Bayesian network learns with a huge number of data, its belief value is updated even if the change in belief is not significant. This causes a problem when the user’s preference changes over time. The learning process cannot catch up rapidly enough to handle ...

We consider the case in which the available knowledge does not allow to specify a precise probabilistic model for the prior and/or likelihood in statistical estimation. We assume that this imprecision can be represented by belief functions. Thus, we exploit the mathematical structure of belief functions and their equivalent representation in terms of closed convex sets of probability measures t...

In the present paper a dynamics for type theory is introduced. The formalization provides epistemic explanations for the basic notions of belief state and belief set by referring to assertion conditions for type-theoretical judgements; it interprets expectations in terms of default assumptions for such a structure and it adapts the usual revision operations and the analogous of the Ramsey test....

This article provides an overview of the audit process along with the belief-function approach to audit decisions. In particular, the article highlights the advantages of using belief functions for representing uncertainties in the audit evidence and discusses the audit risk model of the American Institute of Certified Public Accountants as a plausibility model. Also, the article discusses the ...