This introduction to Bayesian inference places special emphasis on applications. All basic concepts are presented: Bayes' theorem, prior density functions, point estimation, confidence region, hypothesis testing and predictive analysis. In addition, Monte Carlo methods are discussed since the applications mostly rely on the numerical integration of the posterior distribution. Furthermore, Bayesian inference in the linear model, nonlinear model, mixed model and in the model with unknown variance and covariance components is considered. Solutions are supplied for the classification, for the posterior analysis based on distributions of robust maximum likelihood type estimates, and for the reconstruction of digital images.

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Bibliographic Information

Book Title: Bayesian Inference with Geodetic Applications
Authors: Karl-Rudolf Koch
Series Title: Lecture Notes in Earth Sciences
DOI: https://doi.org/10.1007/BFb0048699
Publisher: Springer Berlin, Heidelberg
eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 1990
Softcover ISBN: 978-3-540-53080-0Published: 10 October 1990
eBook ISBN: 978-3-540-46601-7Published: 11 April 2006
Series ISSN: 0930-0317
Series E-ISSN: 1613-2580
Edition Number: 1
Number of Pages: IX, 199
Topics: Geophysics/Geodesy, Geology, Probability Theory and Stochastic Processes, Earth Sciences, general, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences

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Table of contents (17 chapters)

Front Matter

Back Matter

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