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."
General
Imprint: |
Chapman & Hall/CRC
|
Country of origin: |
United States |
Series: |
Chapman & Hall/CRC Applied Environmental Statistics |
Release date: |
August 2012 |
First published: |
2013 |
Authors: |
Eric Parent
• Etienne Rivot
|
Dimensions: |
234 x 156 x 24mm (L x W x T) |
Format: |
Hardcover
|
Pages: |
427 |
Edition: |
New |
ISBN-13: |
978-1-58488-919-9 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
|
LSN: |
1-58488-919-5 |
Barcode: |
9781584889199 |
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