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Introduction to hierarchical bayesian modeling for ecological data Éric Parent, Étienne Rivot

Tipo de material: Libro
 impreso(a) 
 
  y electrónico  
  Libro impreso(a) y electrónico Idioma: Inglés Series Detalles de publicación: Boca Raton, FL CRC Press Taylor & Francis Group c2013Descripción: xxi, 402 páginas fotografías, ilustraciones, mapas, retratos 24 centímetrosISBN:
  • 1584889195
  • 9781584889199
Tema(s): Formatos físicos adicionales: Introduction to hierarchical bayesian modeling for ecological dataClasificación:
  • 574.0727 P3
Recursos en línea: Formatos físicos adicionales disponibles:
  • Disponible en línea
Resumen:
Inglés

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.

Número de sistema: 57765
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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. Inglés

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