Novel Statistical Tools for Conserving and Managing
Populations
By gathering information on key demographic parameters,
scientists can often predict how populations will develop in the
future and relate these parameters to external influences, such as
global warming. Because of their ability to easily incorporate
random effects, fit state-space models, evaluate posterior model
probabilities, and deal with missing data, modern Bayesian methods
have become important in this area of statistical inference and
forecasting.
Emphasising model choice and model averaging, Bayesian Analysis
for Population Ecology presents up-to-date methods for analysing
complex ecological data. Leaders in the statistical ecology field,
the authors apply the theory to a wide range of actual case studies
and illustrate the methods using WinBUGS and R. The computer
programs and full details of the data sets are available on the
book's website.
The first part of the book focuses on models and their
corresponding likelihood functions. The authors examine classical
methods of inference for estimating model parameters, including
maximum-likelihood estimates of parameters using numerical
optimisation algorithms. After building this foundation, the
authors develop the Bayesian approach for fitting models to data.
They also compare Bayesian and traditional approaches to model
fitting and inference.
Exploring challenging problems in population ecology, this book
shows how to use the latest Bayesian methods to analyse data. It
enables readers to apply the methods to their own problems with
confidence.
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