Bayesian Networks: An Introduction provides a self-contained introduction to the theory and
applications of Bayesian networks, a topic of interest and importance for statisticians …

Nowadays, the complex manufacturing processes have to be dynamically modelled and
controlled to optimise the diagnosis and the maintenance policies. This article presents a …

This paper deals with the use of Bayesian networks to compute system reliability. The
reliability analysis problem is described and the usual methods for quantitative reliability …

It is always essential but difficult to capture incomplete, partial or uncertain knowledge when
using ontologies to conceptualize an application domain or to achieve semantic …

This paper presents our ongoing effort on developing a principled methodology for
automatic ontology mapping based on BayesOWL, a probabilistic framework we developed …

Nowadays, the complex manufacturing processes have to be dynamically modelled and
controlled to optimise the diagnosis and the maintenance strategies. The work reported here …

This paper proposes a systematized presentation and a terminology for observations in a
Bayesian network. It focuses on the three main concepts of uncertain evidence, namely …

In order to simplify the information exchange within the medical diagnosis process, a
collaborative software agents framework is presented. The human body systems (eg …

This paper investigates the problem of belief update in Bayesian networks (BN) with
uncertain evidence. Two types of uncertain evidences are identified: virtual evidence …

This chapter offers an accessible introduction to the channel-based approach to Bayesian
probability theory. This framework rests on algebraic and logical foundations, inspired by the …