Constraints occur in many application areas of interest to evolutionary computation. The area considered here is Bayesian networks (BNs), which is a probability-based method for representing and reasoning with uncertain knowledge. This work deals with constraints in BNs and investigates how tournament selection can be adapted to better process such constraints in the context of abductive infere...

Ancestor relations in Bayesian networks (BNs) encode long-range causal relations among random variables. In this paper, we develop dynamic programming (DP) algorithms to compute the exact posterior probabilities of ancestor relations in Bayesian networks. Previous algorithm by Parviainen and Koivisto (2011) evaluates all possible ancestor relations in time O(n3) and space O(3). However, their a...

OBJECTIVE – The objective of this paper is to describe a case study where Bayesian Networks (BNs) were used to construct an expert-based Web effort model. METHOD – We built a single-company BN model solely elicited from expert knowledge, where the domain expert was an experienced Web project manager from a small Web company in Auckland, New Zealand. This model was validated using data from eigh...

Bayesian networks (BNs) provide a neat and compact representation for expressing joint probability distributions (JPDs) and for inference. They are becoming increasingly important in the biological sciences for the tasks of inferring cellular networks [1], modelling protein signalling pathways [2], systems biology, data integration [3], classification [4], and genetic data analysis [5]. The rep...

Many domains require us to reason about system change. Examples include life history data analysis, financial risk modelling, fault diagnosis, and the study of evolution. Reasoning about such systems involves asking questions about event timing, e.g., when will a person find employment. Our answers, best expressed as probability distributions over time, must account for many factors that are, t...

Errors in reasoning about probabilistic evidence can have severe consequences. In the legal domain a number of recent miscarriages of justice emphasises how severe these consequences can be. These cases, in which forensic evidence was misinterpreted, have ignited a scientific debate on how and when probabilistic reasoning can be incorporated in (legal) argumentation. One promising approach is t...

Bayesian networks (BNs) provide a means for representing, displaying, and making available in a usable form the knowledge of experts in a given Weld. In this paper, we look at the performance of an expert constructed BN compared with other machine learning (ML) techniques for predicting the outcome (win, lose, or draw) of matches played by Tottenham Hotspur Football Club. The period under study...

An integrative approach to maritime accident risk factor assessment in accordance with formal safety is proposed, which exploits the multifaceted capabilities of Bayesian networks (BNs) by consolidation modelling, verification, and validation. The methodology for probabilistic modelling BNs well known its application based on model verified though sensitivity analysis only, while validation oft...

1. Background In modelling fault-tolerant systems , space state based approaches such as dynamic fault trees (DFTs) [4], have been shown to increase the power of traditional combinatorial models, like static fault trees (FTs) [9]. However, in practice, these approaches have severe limitations when dealing with the increasing complexity of component dependencies and failure behaviours of today’s...

In the last decade, there has been a growing interest in using Bayesian Networks (BN) in the student modelling problem. This increased interest is probably due to the fact that BNs provide a sound methodology for this difficult task. In order to develop a Bayesian student model, it is necessary to define the structure (nodes and links) and the parameters. Usually the structure can be elicited w...