Juan Chiachío-Ruano is an Associate Professor of Structural Engineering at University of Granada (Spain), and a researcher at the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI). He has devoted his research career to the study and development of Bayesian methods in application to a wide range of Mechanical and Structural Engineering problems. Prior to joining University of Granada, he has developed a significant international research career working at top academic institutions in the UK and the USA.

Manuel Chiachío-Ruano holds a PhD in Structural Engineering (2014) by the University of Granada (Spain). Currently, he is Associate Professor and Head of the Intelligent Prognostics and Cyber-physical Structural Systems Laboratory (iPHMLab) at the University of Granada. He has developed a significant part of his research in collaboration with the California Institute of Technology (USA), the University of Nottingham (UK) and NASA Ames Research Center (USA), during his stays at these institutions.

Shankar Sankararaman received his PhD in Civil Engineering from Vanderbilt University, Nashville, TN, USA, in 2012. Soon after, he joined NASA Ames Research Center, where he developed Machine Learning algorithms and Bayesian methods for system health monitoring, prognostics, decision-making, and uncertainty management. Dr Sankararaman has co-authored a book on prognostics and published over 100 technical articles in international journals and conferences. Presently, Shankar is a scientist at Intuit AI, where he focuses on implementing cutting edge research in products and solutions for Intuit's customers.

Part 1 Fundamentals 1. Introduction to Bayesian Inverse Problems 2. Solving Inverse Problems by Approximate Bayesian Computation 3. Fundamentals of Sequential System Monitoring and Prognostics Methods 4. Parameter Identification Based on Conditional Expectation Part 2 Engineering Applications 5. Sparse Bayesian Learning and its Application in Bayesian System Identification 6. Ultrasonic Guided-waves Based Bayesian Damage Localisation and Optimal Sensor Configuration 7. Fast Bayesian Approach for Stochastic Model Updating using Modal Information from Multiple Setups 8. A Worked-out Example of Surrogate-based Bayesian Parameter and Field Identification Methods