Javier Prieto Tejedor

Javier Prieto received his PhD degree in Information and Communications Technology and the Extraordinary Performance Award for Doctorate Studies in 2012 from the University of Valladolid (Spain). Since 2015, he is a lecturer and researcher at the University of Salamanca (Spain). Previously, he was with the University of Valladolid from 2009 to 2014 and with a Spanish technological center from 2007 to 2009. In 2010, he was a visiting researcher at the Massachusetts Institute of Technology (MIT), USA. Dr. Prieto serves as an associate editor for various journals. His research interests include artificial intelligence for smart cities, navigation for indoor environments, and Bayesian inference for dynamic systems.

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The range of Bayesian inference algorithms and their different applications has been greatly expanded since the first implementation of a Kalman filter by Stanley F. Schmidt for the Apollo program. Extended Kalman filters or particle filters are just some examples of these algorithms that have been extensively applied to logistics, medical services, search and rescue operations, or automotive safety, among others. This book takes a look at both theoretical foundations of Bayesian inference and practical implementations in different fields. It is intended as an introductory guide for the application of Bayesian inference in the fields of life sciences, engineering, and economics, as well as a source document of fundamentals for intermediate Bayesian readers.

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