Introduction to hierarchical bayesian modeling for ecological data / Éric Parent, Étienne Rivot
Por: Parent, Éric [autor/a].
Rivot, Étienne [autor/a].
Tipo de material: Libro impreso(a) y electrónico Series Editor: Boca Raton, FL: CRC Press Taylor & Francis Group, c2013Descripción: xxi, 402 páginas : fotografías, ilustraciones, mapas, retratos ; 24 centímetros.ISBN: 1584889195; 9781584889199.Tema(s): Ecología | Métodos estadísticos | Teoría bayesiana de decisiones estadísticasFormatos físicos adicionales: Introduction to hierarchical bayesian modeling for ecological dataClasificación: 574.0727 / P3 Nota de acceso: Disponible para usuarios de ECOSUR con su clave de acceso Nota de bibliografía: Incluye bibliografía: páginas 375-402 e índice: páginas 403-405 Número de sistema: 57765Resumen:Tipo de ítem | Biblioteca actual | Colección | Signatura | Estado | Fecha de vencimiento | Código de barras |
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Libros |
Biblioteca Chetumal
Texto en configuración de biblioteca Chetumal |
Acervo General | 574.0727 P3 | Disponible | ECO030008524 | |
Libros | Biblioteca Electrónica Recursos en línea (RE) | Acervo General | Recurso digital | ECO400577657325 |
Incluye bibliografía: páginas 375-402 e índice: páginas 403-405
Disponible para usuarios de ECOSUR con su clave de acceso
Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors'website. This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction. eng
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