Bayesian Inference with Geodetic Applications

Karl-Rudolf Koch

ISBN 10: 3540530800 / ISBN 13: 9783540530800

Published by Springer Berlin Heidelberg, 1990

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783540530800

Bibliographic Details

• Title: Bayesian Inference with Geodetic Applications
• Publisher: Springer Berlin Heidelberg
• Publication Date: 1990
• Language: English
• ISBN 10: 3540530800
• ISBN 13: 9783540530800
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 244W x 170H x 12D millimeters 244 millimeters width by 170 millimeters height by 12 millimeters depth
• Weight: 375 grams

About this title

Synopsis

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.