Mathematics

Bayesian Inference

Edited by Javier Prieto Tejedor, ISBN 978-953-51-3578-4, Print ISBN 978-953-51-3577-7, 376 pages, Publisher: InTech, Chapters published November 02, 2017 under CC BY 3.0 license
DOI: 10.5772/66264
Edited Volume

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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