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Bayesian Inference

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  • Intelectual Property Rights
Title:
Bayesian Inference. Editorial.
Authors: 
Prieto Tejedor, Javier
Book:
Editorial. pp. 1-376.

Publication date: 
2 November 2017
ISBN: 
978-953-51-3577-7 (Print), 978-953-51-3578-4 (Online)
DOI
 10.5772/66264
Bayesian Inference</url> <publisher>InTech Open</publisher> </proceedings>"/>

BibTex

@book { book,
title = {Bayesian Inference},
author = {Prieto Tejedor, Javier},
publisher = {InTech Open},
isbn = {978-953-51-3577-7 (Print), 978-953-51-3578-4 (Online)},
year = {2017}
}

XML

<proceedings key='conf/Bayesian/Prieto/2 November 2017' mdate='2 November 2017'>
<editor>Prieto Tejedor</editor>
<editor>Javier</editor>
<title>Bayesian Inference</title>
<pages>1-376</pages>
<year>2017</year>
<ee>10.5772/66264</ee>
<isbn> 978-953-51-3577-7 (Print), 978-953-51-3578-4 (Online) </isbn>
<url>Bayesian Inference</url>
<publisher>InTech Open</publisher>
</proceedings>

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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