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.
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