# Bayesian Population Analysis using WinBUGS

Download and Read online **Bayesian Population Analysis using WinBUGS**, ebooks in PDF, epub, Tuebl Mobi, Kindle Book. Get Free **Bayesian Population Analysis Using WinBUGS** Textbook and unlimited access to our library by created an account. Fast Download speed and ads Free!

## Bayesian Population Analysis Using WinBUGS

Author | : Marc Kéry,Michael Schaub |

Publsiher | : Academic Press |

Total Pages | : 535 |

Release | : 2012 |

ISBN 10 | : 0123870208 |

ISBN 13 | : 9780123870209 |

Language | : EN, FR, DE, ES & NL |

**Bayesian Population Analysis Using WinBUGS Book Review:**

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 Includes complete documentation of all R and WinBUGS code required to conduct analyses and shows all the necessary steps from having the data in a text file out of Excel to interpreting and processing the output from WinBUGS in R

## Bayesian Population Analysis using WinBUGS

Author | : Marc Kery,Michael Schaub |

Publsiher | : Academic Press |

Total Pages | : 554 |

Release | : 2011-10-11 |

ISBN 10 | : 0123870216 |

ISBN 13 | : 9780123870216 |

Language | : EN, FR, DE, ES & NL |

**Bayesian Population Analysis using WinBUGS Book Review:**

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 Includes complete documentation of all R and WinBUGS code required to conduct analyses and shows all the necessary steps from having the data in a text file out of Excel to interpreting and processing the output from WinBUGS in R

## Introduction to WinBUGS for Ecologists

Author | : Marc Kery |

Publsiher | : Academic Press |

Total Pages | : 320 |

Release | : 2010-07-19 |

ISBN 10 | : 9780123786067 |

ISBN 13 | : 0123786061 |

Language | : EN, FR, DE, ES & NL |

**Introduction to WinBUGS for Ecologists Book Review:**

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)

## Integrated Population Models

Author | : Michael Schaub,Marc Kéry |

Publsiher | : Academic Press |

Total Pages | : 838 |

Release | : 2021-07-15 |

ISBN 10 | : 9780323908108 |

ISBN 13 | : 0323908101 |

Language | : EN, FR, DE, ES & NL |

**Integrated Population Models Book Review:**

Integrated Population Models: Theory and Ecological Applications with R and JAGS is the first book on integrated population models, which constitute a powerful framework for combining multiple data sets from the population and the individual levels to estimate demographic parameters, and population size and trends. These models identify drivers of population dynamics and forecast the composition and trajectory of a population. Written by two population ecologists with expertise on integrated population modeling, this book provides a comprehensive synthesis of the relevant theory of integrated population models with an extensive overview of practical applications, using Bayesian methods by means of case studies. The book contains fully-documented, complete code for fitting all models in the free software, R and JAGS. It also includes all required code for pre- and post-model-fitting analysis. Integrated Population Models is an invaluable reference for researchers and practitioners involved in population analysis, and for graduate-level students in ecology, conservation biology, wildlife management, and related fields. The text is ideal for self-study and advanced graduate-level courses. Offers practical and accessible ecological applications of IPMs (integrated population models) Provides full documentation of analyzed code in the Bayesian framework Written and structured for an easy approach to the subject, especially for non-statisticians

## Studyguide for Bayesian Population Analysis Using WinBUGS a Hierarchical Perspective by Kery Marc

Author | : Cram101 |

Publsiher | : Unknown |

Total Pages | : 32 |

Release | : 2021 |

ISBN 10 | : |

ISBN 13 | : OCLC:923493692 |

Language | : EN, FR, DE, ES & NL |

**Studyguide for Bayesian Population Analysis Using WinBUGS a Hierarchical Perspective by Kery Marc Book Review:**

## Bayesian Analysis for Population Ecology

Author | : Ruth King,Byron Morgan,Olivier Gimenez,Steve Brooks |

Publsiher | : CRC Press |

Total Pages | : 456 |

Release | : 2009-10-30 |

ISBN 10 | : 9781439811887 |

ISBN 13 | : 1439811881 |

Language | : EN, FR, DE, ES & NL |

**Bayesian Analysis for Population Ecology Book Review:**

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

## Hierarchical Modeling and Inference in Ecology

Author | : J. Andrew Royle,Robert M. Dorazio |

Publsiher | : Elsevier |

Total Pages | : 464 |

Release | : 2008-10-15 |

ISBN 10 | : 0080559255 |

ISBN 13 | : 9780080559254 |

Language | : EN, FR, DE, ES & NL |

**Hierarchical Modeling and Inference in Ecology Book Review:**

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 Modeling Using WinBUGS

Author | : Ioannis Ntzoufras |

Publsiher | : John Wiley & Sons |

Total Pages | : 520 |

Release | : 2011-09-20 |

ISBN 10 | : 1118210352 |

ISBN 13 | : 9781118210352 |

Language | : EN, FR, DE, ES & NL |

**Bayesian Modeling Using WinBUGS Book Review:**

A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book's related Web site. Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.

## The BUGS Book

Author | : David Lunn,Chris Jackson,Nicky Best,Andrew Thomas,David Spiegelhalter |

Publsiher | : CRC Press |

Total Pages | : 399 |

Release | : 2012-10-02 |

ISBN 10 | : 1466586664 |

ISBN 13 | : 9781466586666 |

Language | : EN, FR, DE, ES & NL |

**The BUGS Book Book Review:**

Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, The BUGS Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity. It also features a large number of worked examples and a wide range of applications from various disciplines. The book introduces regression models, techniques for criticism and comparison, and a wide range of modelling issues before going into the vital area of hierarchical models, one of the most common applications of Bayesian methods. It deals with essentials of modelling without getting bogged down in complexity. The book emphasises model criticism, model comparison, sensitivity analysis to alternative priors, and thoughtful choice of prior distributions—all those aspects of the "art" of modelling that are easily overlooked in more theoretical expositions. More pragmatic than ideological, the authors systematically work through the large range of "tricks" that reveal the real power of the BUGS software, for example, dealing with missing data, censoring, grouped data, prediction, ranking, parameter constraints, and so on. Many of the examples are biostatistical, but they do not require domain knowledge and are generalisable to a wide range of other application areas. Full code and data for examples, exercises, and some solutions can be found on the book’s website.

## Studyguide for Bayesian Population Analysis Using Winbugs

Author | : Cram101 Textbook Reviews |

Publsiher | : Cram101 |

Total Pages | : 154 |

Release | : 2013-05 |

ISBN 10 | : 9781478472193 |

ISBN 13 | : 1478472197 |

Language | : EN, FR, DE, ES & NL |

**Studyguide for Bayesian Population Analysis Using Winbugs Book Review:**

Never HIGHLIGHT a Book Again Includes all testable terms, concepts, persons, places, and events. Cram101 Just the FACTS101 studyguides gives all of the outlines, highlights, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanies: 9780872893795. This item is printed on demand.

## Applied Hierarchical Modeling in Ecology Analysis of distribution abundance and species richness in R and BUGS

Author | : Marc Kery,J. Andrew Royle |

Publsiher | : Academic Press |

Total Pages | : 808 |

Release | : 2015-11-14 |

ISBN 10 | : 0128014865 |

ISBN 13 | : 9780128014868 |

Language | : EN, FR, DE, ES & NL |

**Applied Hierarchical Modeling in Ecology Analysis of distribution abundance and species richness in R and BUGS Book Review:**

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 Methods for Ecology

Author | : Michael A. McCarthy |

Publsiher | : Cambridge University Press |

Total Pages | : 329 |

Release | : 2007-05-10 |

ISBN 10 | : 113946387X |

ISBN 13 | : 9781139463874 |

Language | : EN, FR, DE, ES & NL |

**Bayesian Methods for Ecology Book Review:**

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. Free software is available as well as an accompanying web-site containing the data files and WinBUGS codes. Bayesian Methods for Ecology will appeal to academic researchers, upper undergraduate and graduate students of Ecology.

## Introduction to Probability Simulation and Gibbs Sampling with R

Author | : Eric A. Suess,Bruce E. Trumbo |

Publsiher | : Springer Science & Business Media |

Total Pages | : 307 |

Release | : 2010-06-15 |

ISBN 10 | : 038740273X |

ISBN 13 | : 9780387402734 |

Language | : EN, FR, DE, ES & NL |

**Introduction to Probability Simulation and Gibbs Sampling with R Book Review:**

The first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous states. Applications include coverage probabilities of binomial confidence intervals, estimation of disease prevalence from screening tests, parallel redundancy for improved reliability of systems, and various kinds of genetic modeling. These initial chapters can be used for a non-Bayesian course in the simulation of applied probability models and Markov Chains. Chapters 8 through 10 give a brief introduction to Bayesian estimation and illustrate the use of Gibbs samplers to find posterior distributions and interval estimates, including some examples in which traditional methods do not give satisfactory results. WinBUGS software is introduced with a detailed explanation of its interface and examples of its use for Gibbs sampling for Bayesian estimation. No previous experience using R is required. An appendix introduces R, and complete R code is included for almost all computational examples and problems (along with comments and explanations). Noteworthy features of the book are its intuitive approach, presenting ideas with examples from biostatistics, reliability, and other fields; its large number of figures; and its extraordinarily large number of problems (about a third of the pages), ranging from simple drill to presentation of additional topics. Hints and answers are provided for many of the problems. These features make the book ideal for students of statistics at the senior undergraduate and at the beginning graduate levels.

## Bayesian Models

Author | : N. Thompson Hobbs,Mevin B. Hooten |

Publsiher | : Princeton University Press |

Total Pages | : 320 |

Release | : 2015-08-04 |

ISBN 10 | : 1400866553 |

ISBN 13 | : 9781400866557 |

Language | : EN, FR, DE, ES & NL |

**Bayesian Models Book Review:**

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

## Bayesian Approaches to Clinical Trials and Health Care Evaluation

Author | : David J. Spiegelhalter,Keith R. Abrams,Jonathan P. Myles |

Publsiher | : John Wiley & Sons |

Total Pages | : 408 |

Release | : 2004-01-16 |

ISBN 10 | : 9780471499756 |

ISBN 13 | : 0471499757 |

Language | : EN, FR, DE, ES & NL |

**Bayesian Approaches to Clinical Trials and Health Care Evaluation Book Review:**

READ ALL ABOUT IT! David Spiegelhalter has recently joined the ranks of Isaac Newton, Charles Darwin and Stephen Hawking by becoming a fellow of the Royal Society. Originating from the Medical Research Council’s biostatistics unit, David has played a leading role in the Bristol heart surgery and Harold Shipman inquiries. Order a copy of this author’s comprehensive text TODAY! The Bayesian approach involves synthesising data and judgement in order to reach conclusions about unknown quantities and make predictions. Bayesian methods have become increasingly popular in recent years, notably in medical research, and although there are a number of books on Bayesian analysis, few cover clinical trials and biostatistical applications in any detail. Bayesian Approaches to Clinical Trials and Health-Care Evaluation provides a valuable overview of this rapidly evolving field, including basic Bayesian ideas, prior distributions, clinical trials, observational studies, evidence synthesis and cost-effectiveness analysis. Covers a broad array of essential topics, building from the basics to more advanced techniques. Illustrated throughout by detailed case studies and worked examples Includes exercises in all chapters Accessible to anyone with a basic knowledge of statistics Authors are at the forefront of research into Bayesian methods in medical research Accompanied by a Web site featuring data sets and worked examples using Excel and WinBUGS - the most widely used Bayesian modelling package Bayesian Approaches to Clinical Trials and Health-Care Evaluation is suitable for students and researchers in medical statistics, statisticians in the pharmaceutical industry, and anyone involved in conducting clinical trials and assessment of health-care technology.

## Data Analysis Using Regression and Multilevel Hierarchical Models

Author | : Andrew Gelman,Professor in the Department of Statistics Andrew Gelman,Jennifer Hill |

Publsiher | : Cambridge University Press |

Total Pages | : 625 |

Release | : 2007 |

ISBN 10 | : 9780521686891 |

ISBN 13 | : 052168689X |

Language | : EN, FR, DE, ES & NL |

**Data Analysis Using Regression and Multilevel Hierarchical Models Book Review:**

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

## Bayesian Methods for Statistical Analysis

Author | : Borek Puza |

Publsiher | : ANU Press |

Total Pages | : 679 |

Release | : 2015-10-01 |

ISBN 10 | : 1921934263 |

ISBN 13 | : 9781921934261 |

Language | : EN, FR, DE, ES & NL |

**Bayesian Methods for Statistical Analysis Book Review:**

Bayesian Methods for Statistical Analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code. It is suitable for self-study or a semester-long course, with three hours of lectures and one tutorial per week for 13 weeks.

## Bayesian Data Analysis Third Edition

Author | : Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin |

Publsiher | : CRC Press |

Total Pages | : 675 |

Release | : 2013-11-01 |

ISBN 10 | : 1439840954 |

ISBN 13 | : 9781439840955 |

Language | : EN, FR, DE, ES & NL |

**Bayesian Data Analysis Third Edition Book Review:**

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

## Bayesian Methods for Repeated Measures

Author | : Lyle D. Broemeling |

Publsiher | : CRC Press |

Total Pages | : 568 |

Release | : 2015-08-04 |

ISBN 10 | : 1482248204 |

ISBN 13 | : 9781482248203 |

Language | : EN, FR, DE, ES & NL |

**Bayesian Methods for Repeated Measures Book Review:**

Analyze Repeated Measures Studies Using Bayesian TechniquesGoing beyond standard non-Bayesian books, Bayesian Methods for Repeated Measures presents the main ideas for the analysis of repeated measures and associated designs from a Bayesian viewpoint. It describes many inferential methods for analyzing repeated measures in various scientific areas,

## Bayesian Ideas and Data Analysis

Author | : Ronald Christensen,Wesley Johnson,Adam Branscum,Timothy E Hanson |

Publsiher | : CRC Press |

Total Pages | : 516 |

Release | : 2011-07-07 |

ISBN 10 | : 1439803552 |

ISBN 13 | : 9781439803554 |

Language | : EN, FR, DE, ES & NL |

**Bayesian Ideas and Data Analysis Book Review:**

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book’s website. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions. The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data. Data sets and codes are provided on a supplemental website.