Introduction to hierarchical Bayesian modeling for ecological data
Parent, E.
creator
Rivot, Etienne
text
bibliography
flu
Boca Raton
CRC Press
2013
monographic
eng
xxi, 405 p.
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 authorsâ€™ 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.(https://www.crcpress.com/product/isbn/9781584889199)
Ecology - Statistical methods
Bayesian statistical decision theory
577.0727 P2I6
Chapman & Hall/CRC Applied Environmental Statistics
9781584889199
120525