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Bayesian Essentials with R Jean-Michel Marin; Christian P. Robert

Por: Colaborador(es): Tipo de material: TextoTextoIdioma: Inglés Detalles de publicación: New York, Springer 2014.Edición: 2° edDescripción: xiv, 296 pág.; Figuras; 24 x 16 cmISBN:
  • 9781461486862
Clasificación CDD:
  • 519.542 M337
Contenidos:
User's Manual -- Normal Models -- Regression and Variable Selection -- Generalized Linear Models -- Capture-Recapture Experiments -- Mixture Models -- Time Serie -- Image Analysis
Resumen: The Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. The stake are high and the reader determines the outcome. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models.
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Libros Libros Biblioteca Universidad Regional Amazónica Ikiam 519.542 M337 (Navegar estantería(Abre debajo)) Ej. 1/2 Disponible 005029
Libros Libros Biblioteca Universidad Regional Amazónica Ikiam 519.542 M337 (Navegar estantería(Abre debajo)) Ej. 2/2 Disponible 005030

User's Manual -- Normal Models -- Regression and Variable Selection -- Generalized Linear Models -- Capture-Recapture Experiments -- Mixture Models -- Time Serie -- Image Analysis

The Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. The stake are high and the reader determines the outcome. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models.

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