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...

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...

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 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...