Bayesian Networks in Project Management

Yet B.

in: Wiley StatsRef: Statistics Reference Online, N. Balakrishnan,Theodore Colton,Brian Everitt,Walter Piegorsch,Fabrizio Ruggeri,Jef Teugels, Editor, John Wiley & Sons, West Sussex, UK , New Jersey, pp.1-8, 2017

  • Publication Type: Book Chapter / Chapter Research Book
  • Publication Date: 2017
  • Publisher: John Wiley & Sons, West Sussex, UK 
  • City: New Jersey
  • Page Numbers: pp.1-8
  • Editors: N. Balakrishnan,Theodore Colton,Brian Everitt,Walter Piegorsch,Fabrizio Ruggeri,Jef Teugels, Editor
  • Middle East Technical University Affiliated: No

Abstract

Bayesian networks (BNs) offer unique benefits for combining data and expert knowledge to model complex joint probability distributions. Recent advances in inference algorithms enabled efficient computation of BNs with both discrete and continuous variables that are also called hybrid BNs. Consequently, BNs have been widely used as risk assessment and decision support tools in various domains including project management. This article illustrates the use of BNs in different aspects of project management and gives an overview of the relevant studies in this domain.