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Bayesian inference with geodetic applications [Libro electrónico] / Karl-Rudolf Koch

Por: Koch, Karl-Rudolf [autor/a].
Tipo de material: Libro en línea Libro en línea Series Editor: New York, New York, United States: Springer-Verlag, c1990Descripción: ix, 198 páginas : ilustraciones ; 25 centímetros.ISBN: 3540530800; 0387530800; 9783540530800 (Print); 9783540466017 (Online).Tema(s): Geodesy -- Statistical methods | Bayesian statistical decision theory | Mathematical statisticsNota de acceso: Disponible para usuarios de ECOSUR con su clave de acceso Nota de bibliografía: Incluye bibliografía e índice: páginas 185-189 Número de sistema: 55750Contenidos:Mostrar Resumen:
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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.

Recurso en línea: http://link.springer.com/openurl?genre=book&isbn=978-3-540-53080-0
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Incluye bibliografía e índice: páginas 185-189

1. Introduction.. 2. Basic concepts.. 3. Bayes' theorem.. 4. Prior density functions.. 5. Point estimation.. 6. Confidence regions.. 7. Hypothesis testing.. 8. Predictive analysis.. 9. Numerical techniques.. 10. Models and special applications.. 11. Linear models.. 12. Nonlinear models.. 13. Mixed models.. 14. Linear models with unknown variance and covariance components.. 15. Classification.. 16. Posterior analysis based on distributions for robust maximum likelihood type estimates.. 17. Reconstruction of digital images.. Index

Disponible para usuarios de ECOSUR con su clave de acceso

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. eng

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