Lecture Notes in Earth Sciences

Koch, Karl-Rudolf

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.

Details

Published by: Springer

Publication Date: 1990-10-10

Format: Paperback

ISBN-13: 9783540530800

DOI: 10.1007/BFb0048699

Dimensions: 244cm x170cm

Pages: 199