Bayesian Data Analysis In Ecology Using Linear Models With R Bugs And Stan Including Comparisons To Frequentist Statistics
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Bayesian Data Analysis In Ecology Using Linear Models With R Bugs And Stan
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Author : Franzi Korner-Nievergelt
language : en
Publisher: Academic Press
Release Date : 2015-04-04
Bayesian Data Analysis In Ecology Using Linear Models With R Bugs And Stan written by Franzi Korner-Nievergelt and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-04 with Science categories.
Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types. - Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest - Written in a step-by-step approach that allows for eased understanding by non-statisticians - Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data - All example data as well as additional functions are provided in the R-package blmeco
Bayesian Data Analysis In Ecology Using Linear Models With R Bugs And Stan Including Comparisons To Frequentist Statistics
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Author :
language : en
Publisher:
Release Date :
Bayesian Data Analysis In Ecology Using Linear Models With R Bugs And Stan Including Comparisons To Frequentist Statistics written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with Bayesian statistical decision theory categories.
Bayesian Models For Astrophysical Data
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Author : Joseph M. Hilbe
language : en
Publisher: Cambridge University Press
Release Date : 2017-04-27
Bayesian Models For Astrophysical Data written by Joseph M. Hilbe and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-27 with Mathematics categories.
A hands-on guide to Bayesian models with R, JAGS, Python, and Stan code, for a wide range of astronomical data types.
A Survey Of Methane Emissions From The California Natural Gas System
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Author : Marc L. Fischer
language : en
Publisher:
Release Date : 2017
A Survey Of Methane Emissions From The California Natural Gas System written by Marc L. Fischer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Greenhouse gas mitigation categories.
Atmospheric Measurement And Inverse Modeling To Improve Greenhouse Gas Emission Estimates 2015
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Author : Marc L. Fischer
language : en
Publisher:
Release Date : 2016
Atmospheric Measurement And Inverse Modeling To Improve Greenhouse Gas Emission Estimates 2015 written by Marc L. Fischer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Atmosphere categories.
Ecology Of Invertebrate Diseases
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Author : Ann E. Hajek
language : en
Publisher: John Wiley & Sons
Release Date : 2017-10-27
Ecology Of Invertebrate Diseases written by Ann E. Hajek and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-27 with Science categories.
A rapidly growing interdisciplinary field, disease ecology merges key ideas from ecology, medicine, genetics, immunology, and epidemiology to study how hosts and pathogens interact in populations, communities, and entire ecosystems. Bringing together contributions from leading international experts on the ecology of diseases among invertebrate species, this book provides a comprehensive assessment of the current state of the field. Beginning with an introductory overview of general principles and methodologies, the book continues with in-depth discussions of a range of critical issues concerning invertebrate disease epidemiology, molecular biology, vectors, and pathogens. Topics covered in detail include: Methods for studying the ecology of invertebrate diseases and pathogens Invertebrate pathogen ecology and the ecology of pathogen groups Applied ecology of invertebrate pathogens Leveraging the ecology of invertebrate pathogens in microbial control Prevention and management of infectious diseases of aquatic invertebrates Ecology of Invertebrate Diseases is a necessary and long overdue addition to the world literature on this vitally important subject. This volume belongs on the reference shelves of all those involved in the environmental sciences, genetics, microbiology, marine biology, immunology, epidemiology, fisheries and wildlife science, and related disciplines.
Introduction To Winbugs For Ecologists
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Author : Marc Kéry
language : en
Publisher: Academic Press
Release Date : 2010-07-19
Introduction To Winbugs For Ecologists 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 2010-07-19 with Science categories.
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)
Applied Statistical Modelling For Ecologists
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Author : Marc Kéry
language : en
Publisher: Elsevier
Release Date : 2024-07-18
Applied Statistical Modelling For Ecologists written by Marc Kéry and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-18 with Science categories.
**2025 PROSE Award Finalist in Environmental Science**Applied Statistical Modelling for Ecologists provides a gentle introduction to the essential models of applied statistics: linear models, generalized linear models, mixed and hierarchical models. All models are fit with both a likelihood and a Bayesian approach, using several powerful software packages widely used in research publications: JAGS, NIMBLE, Stan, and TMB. In addition, the foundational method of maximum likelihood is explained in a manner that ecologists can really understand.This book is the successor of the widely used Introduction to WinBUGS for Ecologists (Kéry, Academic Press, 2010). Like its parent, it is extremely effective for both classroom use and self-study, allowing students and researchers alike to quickly learn, understand, and carry out a very wide range of statistical modelling tasks.The examples in Applied Statistical Modelling for Ecologists come from ecology and the environmental sciences, but the underlying statistical models are very widely used by scientists across many disciplines. This book will be useful for anybody who needs to learn and quickly become proficient in statistical modelling, with either a likelihood or a Bayesian focus, and in the model-fitting engines covered, including the three latest packages NIMBLE, Stan, and TMB. - Contains a concise and gentle introduction to probability and applied statistics as needed in ecology and the environmental sciences - Covers the foundations of modern applied statistical modelling - Gives a comprehensive, applied introduction to what currently are the most widely used and most exciting, cutting-edge model fitting software packages: JAGS, NIMBLE, Stan, and TMB - Provides a highly accessible applied introduction to the two dominant methods of fitting parametric statistical models: maximum likelihood and Bayesian posterior inference - Details the principles of model building, model checking and model selection - Adopts a "Rosetta Stone" approach, wherein understanding of one software, and of its associated language, will be greatly enhanced by seeing the analogous code in other engines - Provides all code available for download for students, at https://www.elsevier.com/books-and-journals/book-companion/9780443137150
Bayesian Applications In Environmental And Ecological Studies With R And Stan
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Author : Song S. Qian
language : en
Publisher: CRC Press
Release Date : 2022-08-29
Bayesian Applications In Environmental And Ecological Studies With R And Stan written by Song S. Qian and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-29 with Mathematics categories.
Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data. Features: An accessible overview of Bayesian methods in environmental and ecological studies Emphasizes the hypothetical deductive process, particularly model formulation Necessary background material on Bayesian inference and Monte Carlo simulation Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more Advanced chapter on Bayesian applications, including Bayesian networks and a change point model Complete code for all examples, along with the data used in the book, are available via GitHub The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.
Doing Bayesian Data Analysis
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Author : John Kruschke
language : en
Publisher: Academic Press
Release Date : 2014-11-11
Doing Bayesian Data Analysis written by John Kruschke and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-11 with Mathematics categories.
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes' rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business. - Accessible, including the basics of essential concepts of probability and random sampling - Examples with R programming language and JAGS software - Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks: t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis) - Coverage of experiment planning - R and JAGS computer programming code on website - Exercises have explicit purposes and guidelines for accomplishment - Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs