Introduction to WinBUGS for Ecologists

Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most common statistical distributions: the normal, the uniform, the binomial, and the Poisson. It describes the two different kinds of analysis of variance (ANOVA): one-way and two- or multiway. It looks at the general linear model, or ANCOVA, in R and WinBUGS. It introduces generalized linear model (GLM), i.e., the extension of the normal linear model to allow error distributions other than the normal. The GLM is then extended contain additional sources of random variation to become a generalized linear mixed model (GLMM) for a Poisson example and for a binomial example. The final two chapters showcase two fairly novel and nonstandard versions of a GLMM. The first is the site-occupancy model for species distributions; the second is the binomial (or N-) mixture model for estimation and modeling of abundance. Introduction to the essential theories of key models used by ecologists Complete juxtaposition of classical analyses in R and Bayesian analysis of the same models in WinBUGS Provides every detail of R and WinBUGS code required to conduct all analyses Companion Web Appendix that contains all code contained in the book and additional material (including more code and solutions to exercises)

Produk Detail:

  • Author : Marc Kery
  • Publisher : Academic Press
  • Pages : 320 pages
  • ISBN : 9780123786067
  • Rating : 5/5 from 1 reviews
CLICK HERE TO GET THIS BOOKIntroduction to WinBUGS for Ecologists

Introduction to WinBUGS for Ecologists

Introduction to WinBUGS for Ecologists
  • Author : Marc Kery
  • Publisher : Academic Press
  • Release : 19 July 2010
GET THIS BOOKIntroduction to WinBUGS for Ecologists

Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most

Introduction to WinBUGS for Ecologists

Introduction to WinBUGS for Ecologists
  • Author : Marc Kery
  • Publisher : Academic Press
  • Release : 13 April 2021
GET THIS BOOKIntroduction to WinBUGS for Ecologists

Introduction to WinBUGS for Ecologists is an introduction to Bayesian statistical modeling, written for ecologists by an ecologist, using the widely available and free WinBUGS package. Examples are placed within a comprehensive and largely non-mathematical overview of linear, generalized linear (GLM), linear mixed and generalized linear mixed models (GLMM). This book will be interest to any quantitative scientist who uses regression-type models, especially ecologists, agronomists, geologists, epidemiologists, sociologists, and psychologists.

Bayesian Population Analysis Using WinBUGS

Bayesian Population Analysis Using WinBUGS
  • Author : Marc Kéry,Michael Schaub
  • Publisher : Academic Press
  • Release : 13 April 2021
GET THIS BOOKBayesian Population Analysis Using WinBUGS

Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS, and its open-source sister OpenBugs, is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Comprehensive and richly commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist All WinBUGS/OpenBUGS analyses are completely integrated in software R

Introduction to Hierarchical Bayesian Modeling for Ecological Data

Introduction to Hierarchical Bayesian Modeling for Ecological Data
  • Author : Eric Parent,Etienne Rivot
  • Publisher : CRC Press
  • Release : 21 August 2012
GET THIS BOOKIntroduction to Hierarchical Bayesian Modeling for Ecological Data

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

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
  • Author : Marc Kery,J. Andrew Royle
  • Publisher : Academic Press
  • Release : 10 October 2020
GET THIS BOOKApplied 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, 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

Bayesian Methods for Ecology

Bayesian Methods for Ecology
  • Author : Michael A. McCarthy
  • Publisher : Cambridge University Press
  • Release : 10 May 2007
GET THIS BOOKBayesian Methods for Ecology

The interest in using Bayesian methods in ecology is increasing, however many ecologists have difficulty with conducting the required analyses. McCarthy bridges that gap, using a clear and accessible style. The text also incorporates case studies to demonstrate mark-recapture analysis, development of population models and the use of subjective judgement. The advantages of Bayesian methods, are also described here, for example, the incorporation of any relevant prior information and the ability to assess the evidence in favour of competing hypotheses.

Bayesian Analysis for Population Ecology

Bayesian Analysis for Population Ecology
  • Author : Ruth King,Byron Morgan,Olivier Gimenez,Steve Brooks
  • Publisher : CRC Press
  • Release : 30 October 2009
GET THIS BOOKBayesian Analysis for Population Ecology

Novel Statistical Tools for Conserving and Managing PopulationsBy 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 mode

Ecological Models and Data in R

Ecological Models and Data in R
  • Author : Benjamin M. Bolker
  • Publisher : Princeton University Press
  • Release : 01 July 2008
GET THIS BOOKEcological Models and Data in R

Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R. Drawing on extensive experience teaching these techniques to graduate students in ecology, Benjamin Bolker shows how to choose among and construct statistical models for data, estimate

Bayesian Models

Bayesian Models
  • Author : N. Thompson Hobbs,Mevin B. Hooten
  • Publisher : Princeton University Press
  • Release : 04 August 2015
GET THIS BOOKBayesian Models

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

Mixed Effects Models and Extensions in Ecology with R

Mixed Effects Models and Extensions in Ecology with R
  • Author : Alain Zuur,Elena N. Ieno,Neil Walker,Anatoly A. Saveliev,Graham M. Smith
  • Publisher : Springer Science & Business Media
  • Release : 05 March 2009
GET THIS BOOKMixed Effects Models and Extensions in Ecology with R

This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive modelling, GAM and GLM, and then discusses dealing with spatial or temporal dependencies and nested data.

Hierarchical Modeling and Inference in Ecology

Hierarchical Modeling and Inference in Ecology
  • Author : J. Andrew Royle,Robert M. Dorazio
  • Publisher : Elsevier
  • Release : 15 October 2008
GET THIS BOOKHierarchical Modeling and Inference in Ecology

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

Bayesian Analysis for Population Ecology

Bayesian Analysis for Population Ecology
  • Author : Ruth King,Byron Morgan,Olivier Gimenez,Steve Brooks
  • Publisher : CRC Press
  • Release : 30 October 2009
GET THIS BOOKBayesian Analysis for Population Ecology

Novel Statistical Tools for Conserving and Managing PopulationsBy 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 mode

Spatial Capture-Recapture

Spatial Capture-Recapture
  • Author : J. Andrew Royle,Richard B. Chandler,Rahel Sollmann,Beth Gardner
  • Publisher : Academic Press
  • Release : 27 August 2013
GET THIS BOOKSpatial Capture-Recapture

Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and knowledge. Spatial Capture-Recapture provides you with an extensive step-by-step analysis of many data sets using different software implementations. The authors' approach is practical – it embraces Bayesian and classical inference strategies to give the reader different options to get the job done. In addition, Spatial Capture-Recapture provides data sets, sample code and computing scripts in an R package. Comprehensive reference on revolutionary

Occupancy Estimation and Modeling

Occupancy Estimation and Modeling
  • Author : Darryl I. MacKenzie
  • Publisher : Academic Press
  • Release : 13 April 2021
GET THIS BOOKOccupancy Estimation and Modeling

Occupancy in ecological investigations; Fundamental principles of statistical inference; Single-species, single-season occupancy models; Single-species, single-season models with heterogeneous detection probabilities; Design of single-season occupancy studies; Single-species, multiple-season occupancy models; Occupancy data for multiple species: species interactions; Occupancy in community-level studies; Future directions.