Hierarchical Modelling for the Environmental Sciences - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Hierarchical Modelling for the Environmental Sciences write by James Samuel Clark. This book was released on 2006. Hierarchical Modelling for the Environmental Sciences available in PDF, EPUB and Kindle. New statistical tools are changing the ways in which scientists analyze and interpret data and models. Many of these are emerging as a result of the wide availability of inexpensive, high speed computational power. In particular, hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complex, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences. Models have developed rapidly, and there is now a requirement for a clear exposition of the methodology through to application for a range of environmental challenges.
Hierarchical Modeling and Inference in Ecology
Hierarchical Modeling and Inference in Ecology - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Hierarchical Modeling and Inference in Ecology write by J. Andrew Royle. This book was released on 2008-10-15. Hierarchical Modeling and Inference in Ecology available in PDF, EPUB and Kindle. A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants) Development of classical, likelihood-based procedures for inference, as well as Bayesian methods of analysis Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS Computing support in technical appendices in an online companion web site
Hierarchical Modelling for the Environmental Sciences
Hierarchical Modelling for the Environmental Sciences - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Hierarchical Modelling for the Environmental Sciences write by James S. Clark. This book was released on 2006-05-04. Hierarchical Modelling for the Environmental Sciences available in PDF, EPUB and Kindle. New statistical tools are changing the ways in which scientists analyze and interpret data and models. Many of these are emerging as a result of the wide availability of inexpensive, high speed computational power. In particular, hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complex, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences. Models have developed rapidly, and there is now a requirement for a clear exposition of the methodology through to application for a range of environmental challenges.
Introduction to Hierarchical Bayesian Modeling for Ecological Data
Introduction to Hierarchical Bayesian Modeling for Ecological Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Introduction to Hierarchical Bayesian Modeling for Ecological Data write by Eric Parent. This book was released on 2012-08-21. Introduction to Hierarchical Bayesian Modeling for Ecological Data available in PDF, EPUB and Kindle. 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.
Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS
Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS write by Marc Kéry. This book was released on 2015-11-14. Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS available in PDF, EPUB and Kindle. Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications in such areas as biodiversity monitoring and fisheries and wildlife management. This first volume explains static models/procedures in the context of hierarchical models that collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models. Applied Hierarchical Modeling in Ecology, Volume 1 serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields. Provides a synthesis of important classes of models about distribution, abundance, and species richness while accommodating imperfect detection Presents models and methods for identifying unmarked individuals and species Written in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analyses Includes companion website containing data sets, code, solutions to exercises, and further information
