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Bayesian inference with geodetic applications Libro electrónico Karl-Rudolf Koch

Tipo de material: Libro
 en línea Libro en línea Idioma: Inglés Series Detalles de publicación: New York, New York, United States Springer-Verlag c1990Descripción: ix, 198 páginas ilustraciones 25 centímetrosISBN:
  • 3540530800
  • 0387530800
  • 9783540530800 (Print)
  • 9783540466017 (Online)
Tema(s): Recursos en línea: Formatos físicos adicionales disponibles:
  • Disponible en línea
Indice:Mostrar
Resumen:
Inglés

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.

Número de sistema: 55750
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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

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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. Inglés

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