Bayesian Inference with Geodetic Applications
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"Bayesian Inference with Geodetic Applications" by Karl-Rudolf Koch is a mathematics book and learning resource focused on Astronomical Data. Best for teachers, students, and readers looking for stronger mathematical understanding.
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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"Bayesian Inference with Geodetic Applications" by Karl-Rudolf Koch is a mathematics book and learning resource focused on Astronomical Data. Best for teachers, students, and readers looking for stronger mathematical understanding.
Topic: Astronomical Data
Author: Karl-Rudolf Koch
Who this is for:
- Teachers and classroom instructors
- Students building subject mastery
- Readers looking for practical learning support
Why this book matters: It stands out as a practical math resource that helps explain concepts, strengthen problem-solving, and support classroom or independent learning.
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.
| Author | Karl-Rudolf Koch |
| Publisher | Springer |
| Published | 1990 |
| ISBN-13 | 9783540530800 |
| Binding | Paperback |
| Pages | 212 |
| Language | English |
| Subjects | Science |
| Topic | Astronomical Data |
| Series | Lecture Notes in Earth Sciences |
Format: Paperback
Length: 212 pages
Language: English
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