Introduction To Hierarchical Bayesian Modeling For Ecological Data
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Introduction To Hierarchical Bayesian Modeling For Ecological Data
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Author : Eric Parent
language : en
Publisher: CRC Press
Release Date : 2012-08-21
Introduction To Hierarchical Bayesian Modeling For Ecological Data written by Eric Parent and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-21 with Mathematics categories.
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 statisti
Models For Ecological Data
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Author : James S. Clark
language : en
Publisher: Princeton University Press
Release Date : 2020-10-06
Models For Ecological Data written by James S. Clark and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-06 with Science categories.
The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data. Consistent treatment from classical to modern Bayes Underlying distribution theory to algorithm development Many examples and applications Does not assume statistical background Extensive supporting appendixes Lab manual in R is available separately
Hierarchical Modeling And Inference In Ecology
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Author : J. Andrew Royle
language : en
Publisher: Elsevier
Release Date : 2008-10-15
Hierarchical Modeling And Inference In Ecology written by J. Andrew Royle and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-15 with Science categories.
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
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Author : James Samuel Clark
language : en
Publisher:
Release Date : 2023
Hierarchical Modelling For The Environmental Sciences written by James Samuel Clark and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Bayesian statistical decision theory categories.
New statistical tools are changing the ways in which scientists analyse and interpret data and models. This text provides a non-technical overview of hierarchical Bayes and Markov Chain Monte Carlo (MCMC) methods for analysis of environmental data.
Introduction To Bayesian Methods In Ecology And Natural Resources
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Author : Edwin J. Green
language : en
Publisher: Springer Nature
Release Date : 2020-11-26
Introduction To Bayesian Methods In Ecology And Natural Resources written by Edwin J. Green and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-26 with Science categories.
This book presents modern Bayesian analysis in a format that is accessible to researchers in the fields of ecology, wildlife biology, and natural resource management. Bayesian analysis has undergone a remarkable transformation since the early 1990s. Widespread adoption of Markov chain Monte Carlo techniques has made the Bayesian paradigm the viable alternative to classical statistical procedures for scientific inference. The Bayesian approach has a number of desirable qualities, three chief ones being: i) the mathematical procedure is always the same, allowing the analyst to concentrate on the scientific aspects of the problem; ii) historical information is readily used, when appropriate; and iii) hierarchical models are readily accommodated. This monograph contains numerous worked examples and the requisite computer programs. The latter are easily modified to meet new situations. A primer on probability distributions is also included because these form the basis of Bayesian inference. Researchers and graduate students in Ecology and Natural Resource Management will find this book a valuable reference.
Applied Hierarchical Modeling In Ecology Analysis Of Distribution Abundance And Species Richness In R And Bugs
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Author : Marc Kéry
language : en
Publisher: Academic Press
Release Date : 2015-11-14
Applied Hierarchical Modeling In Ecology Analysis Of Distribution Abundance And Species Richness In R And Bugs written by Marc Kéry and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-14 with Science categories.
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
Bayesian Models
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Author : N. Thompson Hobbs
language : en
Publisher: Princeton University Press
Release Date : 2015-08-04
Bayesian Models written by N. Thompson Hobbs and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-04 with Science categories.
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals. This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management. Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticians Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more Deemphasizes computer coding in favor of basic principles Explains how to write out properly factored statistical expressions representing Bayesian models
Bringing Bayesian Models To Life
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Author : Mevin B. Hooten
language : en
Publisher: CRC Press
Release Date : 2019-05-15
Bringing Bayesian Models To Life written by Mevin B. Hooten and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-15 with Science categories.
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.
Applied Hierarchical Modeling In Ecology Analysis Of Distribution Abundance And Species Richness In R And Bugs
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Author : Marc Kery
language : en
Publisher: Academic Press
Release Date : 2020-10-10
Applied Hierarchical Modeling In Ecology Analysis Of Distribution Abundance And Species Richness In R And Bugs written by Marc Kery and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-10 with Nature categories.
Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS, Volume Two: Dynamic and Advanced Models provides a synthesis of the state-of-the-art in hierarchical models for plant and animal distribution, also focusing on the complex and more advanced models currently available. The book explains all procedures in the context of hierarchical models that represent a unified approach to ecological research, thus taking the reader from design, through data collection, and into analyses using a very powerful way of synthesizing data. Makes ecological modeling accessible to people who are struggling to use complex or advanced modeling programs Synthesizes current ecological models and explains how they are inter-connected Contains numerous examples throughout the book, walking the reading through scenarios with both real and simulated data Provides an ideal resource for ecologists working in R software and in BUGS software for more flexible Bayesian analyses
Hierarchical Modeling And Analysis For Spatial Data
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Author : Sudipto Banerjee
language : en
Publisher: CRC Press
Release Date : 2003-12-17
Hierarchical Modeling And Analysis For Spatial Data written by Sudipto Banerjee and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-12-17 with Mathematics categories.
Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis,