Bayesian Inference with Geodetic Applications

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 a...

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Bibliographic Details
Main Author: Koch, Karl-Rudolf
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 1990, 1990
Edition:1st ed. 1990
Series:Lecture Notes in Earth Sciences
Subjects:
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
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245 0 0 |a Bayesian Inference with Geodetic Applications  |h Elektronische Ressource  |c by Karl-Rudolf Koch 
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505 0 |a Basic concepts -- Bayes’ Theorem -- Prior density functions -- Point estimation -- Confidence regions -- Hypothesis testing -- Predictive analysis -- Numerical techniques -- Models and special applications -- Linear models -- Nonlinear models -- Mixed models -- Linear models with unknown variance and covariance components -- Classification -- Posterior analysis based on distributions for robust maximum likelihood type estimates -- Reconstruction of digital images.  
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653 |a Earth sciences 
653 |a Probability Theory and Stochastic Processes 
653 |a Geophysics/Geodesy 
653 |a Probabilities 
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989 |b SBA  |a Springer Book Archives -2004 
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520 |a 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