Bayesian Analysis For Population Ecology (chapman & Hall/crc Interdisciplinary Statistics)
by Steve Brooks /
2009 / English / PDF
3.6 MB Download
Novel Statistical Tools for Conserving and Managing
Populations
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.
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,
Emphasising model choice and model averaging,Bayesian
Analysis for Population Ecology
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.
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.
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.
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.