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Bayesian Inference with Geodetic Applications

  • Book
  • © 1990

Overview

Authors:
  1. Karl-Rudolf Koch
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Part of the book series: Lecture Notes in Earth Sciences (LNEARTH, volume 31)

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Table of contents (17 chapters)

  1. Front Matter

    Pages I-IX
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  2. Introduction

    • Karl-Rudolf Koch
    Pages 1-2

  3. Basic concepts

    • Karl-Rudolf Koch
    Pages 3-3

  4. Bayes’ Theorem

    • Karl-Rudolf Koch
    Pages 4-8

  5. Prior density functions

    • Karl-Rudolf Koch
    Pages 9-32

  6. Point estimation

    • Karl-Rudolf Koch
    Pages 33-36

  7. Confidence regions

    • Karl-Rudolf Koch
    Pages 37-39

  8. Hypothesis testing

    • Karl-Rudolf Koch
    Pages 40-48

  9. Predictive analysis

    • Karl-Rudolf Koch
    Pages 49-51

  10. Numerical techniques

    • Karl-Rudolf Koch
    Pages 52-60

  11. Models and special applications

    • Karl-Rudolf Koch
    Pages 61-61

  12. Linear models

    • Karl-Rudolf Koch
    Pages 62-98

  13. Nonlinear models

    • Karl-Rudolf Koch
    Pages 99-108

  14. Mixed models

    • Karl-Rudolf Koch
    Pages 109-121

  15. Linear models with unknown variance and covariance components

    • Karl-Rudolf Koch
    Pages 122-134

  16. Classification

    • Karl-Rudolf Koch
    Pages 135-143

  17. Posterior analysis based on distributions for robust maximum likelihood type estimates

    • Karl-Rudolf Koch
    Pages 144-155

  18. Reconstruction of digital images

    • Karl-Rudolf Koch
    Pages 156-167

  19. Back Matter

    Pages 169-198
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Keywords

About this book

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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