Cutset conditioning and clique-tree propagation are two popular methods for exact probabilistic inference in Bayesian belief networks. Cutset conditioning is based on decomposition of a subset of network nodes, whereas clique-tree propagation depends on aggregation of nodes. We characterize network structures in which the performances of these methods differ. We describe a means to combine cuts...

Conspiracy theories cover topics from politicians to world events. Frequently, proponents of conspiracies hold these beliefs strongly despite available evidence that may challenge or disprove them. Therefore, conspiratorial reasoning has often been described as illegitimate or flawed. In the paper, we explore the possibility of growing a rational (Bayesian) conspiracy theorist through an Agent-...

This paper presents an eficient algorithm for learning a Bayesian belief network (BBN) structure from a database, as well as providing a comparison between two BBN structure fitness functions. A Bayesian belief network is a directed acyclic graph representing conditional expectations. In this paper, we propose a two-phase algorithm. The first phase uses asymptotically correct structure learning...

Graphical models have become common for representing probabilistic models in statistics and artificial intelligence. A Bayesian network is a graphical model which encodes a probability model as a directed graph in which nodes correspond to random variables, together with a set of conditional distributions of nodes given their parents. In most current applications of Bayesian networks, a fixed n...

This paper proposes machine learning techniques to discover knowledge in a dataset in the form of if-then rules for the purpose of formulating queries for validation of a Bayesian belief network model of the same data. Although domain expertise is often available, the query formulation task is tedious and laborious, and hence automation of query formulation is desirable. In an effort to automat...

While inspections are a valuable tool for software quality assurance, inspection models are labour intensive, require knowledge of all errors in a software product, make questionable assumptions, and do not capture the experience of inspectors. In this paper we describe a novel inspection model based on Bayesian belief networks that overcomes many of these problems. We describe the problems whi...

Peter Sember and Ingrid Zukerman Department of Computer Science Monash University Oayton, VICfORIA 3168, AUSTRALIA netmail address: [email protected]@seismo.css.gov [email protected]@seismo.css.gov Bayesian Belief Networks have been largely overlooked by Expert Systems practitioners on the grounds that they do not correspond to the human inference mechanism. In this paper, we intro...

In this paper we propose a novel method to solve a kidnapped robot localization problem. A mobile robot plans its sensing action for localization using learned Bayesian network’s inference. Concretely, we represent the contextual relation between the local sensing results, actions and the global localization beliefs using the Bayesian network. The Bayesian network structure is learned from comp...

This paper describes a new framework for using natural selection to evolve Bayesian Networks for use in forecasting time series data. It extends current research by introducing a tree based representation of a candidate Bayesian Network that addresses the problem of model identification and training through the use of natural selection. The framework constructs a modified Naïve Bayesian classif...