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Introduction to Hierarchical Bayesian Modeling for Ecological Data

2012-08-21 Mathematics
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Release : 2012-08-21
Genre : Mathematics
Kind : eBook
Book Rating : 209/5 ( reviews)

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Book Synopsis Introduction to Hierarchical Bayesian Modeling for Ecological Data by : Eric Parent

Download or read book Introduction to Hierarchical Bayesian Modeling for Ecological Data written by Eric Parent. This book was released on 2012-08-21. Available in PDF, EPUB and Kindle. Book excerpt: 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

Bayesian Models

2015-08-04 Science
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Release : 2015-08-04
Genre : Science
Kind : eBook
Book Rating : 553/5 ( reviews)

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Book Synopsis Bayesian Models by : N. Thompson Hobbs

Download or read book Bayesian Models written by N. Thompson Hobbs. This book was released on 2015-08-04. Available in PDF, EPUB and Kindle. Book excerpt: 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

Hierarchical Modeling and Inference in Ecology

2008-10-15 Science
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Release : 2008-10-15
Genre : Science
Kind : eBook
Book Rating : 255/5 ( reviews)

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Book Synopsis Hierarchical Modeling and Inference in Ecology by : J. Andrew Royle

Download or read book Hierarchical Modeling and Inference in Ecology written by J. Andrew Royle. This book was released on 2008-10-15. Available in PDF, EPUB and Kindle. Book excerpt: 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

Bayesian Hierarchical Models for Ecological Data

2023
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Release : 2023
Genre :
Kind : eBook
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Book Synopsis Bayesian Hierarchical Models for Ecological Data by : Fabian Ricardo Ketwaroo

Download or read book Bayesian Hierarchical Models for Ecological Data written by Fabian Ricardo Ketwaroo. This book was released on 2023. Available in PDF, EPUB and Kindle. Book excerpt:

Models for Ecological Data

2020-10-06 Science
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Release : 2020-10-06
Genre : Science
Kind : eBook
Book Rating : 123/5 ( reviews)

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Book Synopsis Models for Ecological Data by : James S. Clark

Download or read book Models for Ecological Data written by James S. Clark. This book was released on 2020-10-06. Available in PDF, EPUB and Kindle. Book excerpt: 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

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