The structure and parameters of a belief network are learned in order to classify images enabling the detection of genetic abnormalities. We compare a structure learned from the data to another structure obtained utilizing expert knowledge and to the naive Bayesian classifier and study quantization in comparison to density estimation in parameter learning. 2004 Elsevier B.V. All rights reserved.

Kripke structures have been proposed as a semantic basis for modal logics of necessity and possibility. They consist of a set of states, informally interpreted as "possible worlds", and a binary accessibility relation between states. The primitive notion of a possible world in this context seems highly intuitive, since necessity can be interpreted as "truth in all possible worlds", and possibil...

To act optimally in a partially observable, stochastic and multi-agent environment, an autonomous agent needs to maintain a belief of the world at any given time. An extension of partially observable Markov decision processes (POMDPs), called interactive POMDPs (I-POMDPs), provides a principled framework for planning and acting in such settings. I-POMDP augments the POMDP beliefs by including m...

In recent work, Stalnaker proposes a logical framework in which belief is realized as a weakened form of knowledge [33]. Building on Stalnaker’s core insights, and using frameworks developed in [12] and [4], we employ topological tools to refine and, we argue, improve on this analysis. The structure of topological subset spaces allows for a natural distinction between what is known and (roughly...

In recent work, Stalnaker proposes a logical framework in which belief is realized as a weakened form of knowledge [30]. Building on Stalnaker’s core insights, and using frameworks developed in [11] and [4], we employ topological tools to refine and, we argue, improve on this analysis. The structure of topological subset spaces allows for a natural distinction between what is known and (roughly...

This paper provides an overview of distributed belief networks and how they can model engineering systems. In extending the usual single-processor computational model to multiple processors, several interesting problems arise, which must be solved for the successful implementation of a distributed belief network. In keeping with the distributed computational model, no single processor has infor...

Some accounts of mental content represent the objects of belief as structured, using entities that formally resemble the sentences used to express and report attitudes in natural language; others adopt a relatively unstructured approach, typically using sets or functions. Currently popular variants of the latter include classical and neoclassical propositionalism, which represent belief content...

Multiply sectioned Bayesian networks (MSBNs) support multiagent probabilistic inference in distributed large problem domains, where agents (subdomains) are organized by a tree structure (called hypertree). In earlier work, all belief updating methods on a hypertree are made of two rounds of propagation, each of which is implemented as a recursive process. Both processes need to be started from ...

The goal of this study was to assess the relationship between the Five Factor Model of personality and the belief in the paranormal. Participants (N=307) were students from the University of Zagreb, Croatia. The measures used were the IPIP version of the Five Factor Model questionnaire (Goldberg et al., 2006) along with the Revised Paranormal Belief Scale (Tobacyk, 1988). The factor analysis of...