Bayesian network is the natural tool for engineering risk analysts

Daniel Straub, Technical University of Munich
Thursday 18th July 2024 4:30pm-5:15pm AEST

In engineering, a purely data-driven risk analysis is typically not an option because of rare failure events and the uniqueness of many systems. Therefore, engineering risk analysis typically requires a combination of models, data, and expert knowledge. At this BNMA July Webinar, Professor Daniel Straub will discuss that the BN is an ideal modeling tool in this context. It facilitates combining different models and consistently integrating information from various sources. Because of its graphical representation, it is also ideal for validation and communication. Daniel will show the potential and benefits of BN on several engineering risk analysis examples. He will also highlight some of the pitfalls and common misunderstandings, such as the confusion between diagnostic and causal reasoning, and discuss how to deal with them.

Daniel is Professor for engineering risk and reliability analysis at Technische Universität München (TU München). His interest is in developing physics-based stochastic models and methods for decision support and risk analysis of engineering systems. He uses BNs frequently for modeling and communicating uncertainties, both in research and practical applications.