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Bringing Bayesian Models to Life empowers the reader to extend,
enhance, and implement statistical models for ecological and
environmental data analysis. We open the black box and show the
reader how to connect modern statistical models to computer
algorithms. These algorithms allow the user to fit models that
answer their scientific questions without needing to rely on
automated Bayesian software. We show how to handcraft statistical
models that are useful in ecological and environmental science
including: linear and generalized linear models, spatial and time
series models, occupancy and capture-recapture models, animal
movement models, spatio-temporal models, and integrated
population-models. Features: R code implementing algorithms to fit
Bayesian models using real and simulated data examples. A
comprehensive review of statistical models commonly used in
ecological and environmental science. Overview of Bayesian
computational methods such as importance sampling, MCMC, and HMC.
Derivations of the necessary components to construct statistical
algorithms from scratch. Bringing Bayesian Models to Life contains
a comprehensive treatment of models and associated algorithms for
fitting the models to data. We provide detailed and annotated R
code in each chapter and apply it to fit each model we present to
either real or simulated data for instructional purposes. Our code
shows how to create every result and figure in the book so that
readers can use and modify it for their own analyses. We provide
all code and data in an organized set of directories available at
the authors' websites.
The study of animal movement has always been a key element in
ecological science, because it is inherently linked to critical
processes that scale from individuals to populations and
communities to ecosystems. Rapid improvements in biotelemetry data
collection and processing technology have given rise to a variety
of statistical methods for characterizing animal movement. The book
serves as a comprehensive reference for the types of statistical
models used to study individual-based animal movement. Animal
Movement is an essential reference for wildlife biologists,
quantitative ecologists, and statisticians who seek a deeper
understanding of modern animal movement models. A wide variety of
modeling approaches are reconciled in the book using a consistent
notation. Models are organized into groups based on how they treat
the underlying spatio-temporal process of movement. Connections
among approaches are highlighted to allow the reader to form a
broader view of animal movement analysis and its associations with
traditional spatial and temporal statistical modeling. After an
initial overview examining the role that animal movement plays in
ecology, a primer on spatial and temporal statistics provides a
solid foundation for the remainder of the book. Each subsequent
chapter outlines a fundamental type of statistical model utilized
in the contemporary analysis of telemetry data for animal movement
inference. Descriptions begin with basic traditional forms and
sequentially build up to general classes of models in each
category. Important background and technical details for each class
of model are provided, including spatial point process models,
discrete-time dynamic models, and continuous-time stochastic
process models. The book also covers the essential elements for how
to accommodate multiple sources of uncertainty, such as location
error and latent behavior states. In addition to thorough
descriptions of animal movement models, differences and connections
are also emphasized to provide a broader perspective of approaches.
The study of animal movement has always been a key element in
ecological science, because it is inherently linked to critical
processes that scale from individuals to populations and
communities to ecosystems. Rapid improvements in biotelemetry data
collection and processing technology have given rise to a variety
of statistical methods for characterizing animal movement. The book
serves as a comprehensive reference for the types of statistical
models used to study individual-based animal movement. Animal
Movement is an essential reference for wildlife biologists,
quantitative ecologists, and statisticians who seek a deeper
understanding of modern animal movement models. A wide variety of
modeling approaches are reconciled in the book using a consistent
notation. Models are organized into groups based on how they treat
the underlying spatio-temporal process of movement. Connections
among approaches are highlighted to allow the reader to form a
broader view of animal movement analysis and its associations with
traditional spatial and temporal statistical modeling. After an
initial overview examining the role that animal movement plays in
ecology, a primer on spatial and temporal statistics provides a
solid foundation for the remainder of the book. Each subsequent
chapter outlines a fundamental type of statistical model utilized
in the contemporary analysis of telemetry data for animal movement
inference. Descriptions begin with basic traditional forms and
sequentially build up to general classes of models in each
category. Important background and technical details for each class
of model are provided, including spatial point process models,
discrete-time dynamic models, and continuous-time stochastic
process models. The book also covers the essential elements for how
to accommodate multiple sources of uncertainty, such as location
error and latent behavior states. In addition to thorough
descriptions of animal movement models, differences and connections
are also emphasized to provide a broader perspective of approaches.
Bringing Bayesian Models to Life empowers the reader to extend,
enhance, and implement statistical models for ecological and
environmental data analysis. We open the black box and show the
reader how to connect modern statistical models to computer
algorithms. These algorithms allow the user to fit models that
answer their scientific questions without needing to rely on
automated Bayesian software. We show how to handcraft statistical
models that are useful in ecological and environmental science
including: linear and generalized linear models, spatial and time
series models, occupancy and capture-recapture models, animal
movement models, spatio-temporal models, and integrated
population-models. Features: R code implementing algorithms to fit
Bayesian models using real and simulated data examples. A
comprehensive review of statistical models commonly used in
ecological and environmental science. Overview of Bayesian
computational methods such as importance sampling, MCMC, and HMC.
Derivations of the necessary components to construct statistical
algorithms from scratch. Bringing Bayesian Models to Life contains
a comprehensive treatment of models and associated algorithms for
fitting the models to data. We provide detailed and annotated R
code in each chapter and apply it to fit each model we present to
either real or simulated data for instructional purposes. Our code
shows how to create every result and figure in the book so that
readers can use and modify it for their own analyses. We provide
all code and data in an organized set of directories available at
the authors' websites.
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