Bayesian Models

A Statistical Primer for Ecologists

Author: N. Thompson Hobbs,Mevin B. Hooten

Publisher: Princeton University Press

ISBN: 1400866553

Category: Science

Page: 320

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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 Models

A Statistical Primer for Ecologists

Author: N. Thompson Hobbs,Mevin B. Hooten

Publisher: N.A

ISBN: 9780691159287

Category: Mathematics

Page: 320

View: 6456

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"This pitch-perfect exposition shows how Bayesian modeling can be used to quantify our uncertain world. Ecologists--and for that matter, scientists everywhere--are aware of these uncertainties, and this book gives them the understanding to do something about it. Hobbs and Hooten take us on a signposted journey through the culture, construction, and consequences of conditional-probability modeling, readying us to take our own scientific journeys through uncertain landscapes."--Noel Cressie, University of Wollongong, Australia "Hobbs and Hooten provide a complete guide to Bayesian thinking and statistics. This is a book by ecologists for ecologists. One of the powers of Bayesian thinking is how it enables you to evaluate knowledge accumulated through multiple experiments and publications, and this excellent primer provides a firm grounding in the hierarchical models that are now the standard approach to evaluating disparate data sets."--Ray Hilborn, University of Washington "In this uniquely well-written and accessible text, Hobbs and Hooten show how to think clearly in a Bayesian framework about data, models, and linking data with models. They provide the necessary tools to develop, implement, and analyze a wide range of ecologically interesting models. There's something new and exciting in this book for every practicing ecologist."--Aaron M. Ellison, Harvard University "Hobbs and Hooten provide an important bridge between standard statistical texts and more advanced Bayesian books, even those aimed at ecologists. Ecological models are complex. Building from likelihood to simple and hierarchical Bayesian models, the authors do a superb job of focusing on concepts, from philosophy to the necessary mathematical and statistical tools. This practical and understandable book belongs on the shelves of all scientists and statisticians interested in ecology."--Jay M. Ver Hoef, Statistician, NOAA-NMFS Alaska Fisheries Science Center "Tackling an important and challenging topic, Hobbs and Hooten provide non-statistically-trained ecologists with the skills they need to use hierarchical Bayesian models accurately and comfortably. The combination of technical explanations and practical examples is great. This book is a valuable contribution that will be widely used."--Benjamin Bolker, McMaster University "This excellent book is one of the best-written and most complete primers on Bayesian hierarchical modeling I have seen. Hobbs and Hooten anticipate many of the common pitfalls and concerns that arise when non-statisticians are introduced to this material. Researchers across a wide range of disciplines will find this book valuable."--Christopher Wikle, University of Missouri

Bayesian Methods for Ecology

Author: Michael A. McCarthy

Publisher: Cambridge University Press

ISBN: 113946387X

Category: Science

Page: N.A

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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.

Ecological Models and Data in R

Author: Benjamin M. Bolker

Publisher: Princeton University Press

ISBN: 0691125228

Category: Computers

Page: 396

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Introduction and background; Exploratory data analysis and graphics; Deterministic functions for ecological modeling; Probability and stochastic distributions for ecological modeling; Stochatsic simulation and power analysis; Likelihood and all that; Optimization and all that; Likelihood examples; Standar statistics revisited; Modeling variance; Dynamic models.

Introduction to WinBUGS for Ecologists

Bayesian Approach to Regression, ANOVA, Mixed Models and Related Analyses

Author: Marc Kery

Publisher: Academic Press

ISBN: 9780123786067

Category: Science

Page: 320

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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)

Introduction to Hierarchical Bayesian Modeling for Ecological Data

Author: Eric Parent,Etienne Rivot

Publisher: CRC Press

ISBN: 1584889195

Category: Mathematics

Page: 427

View: 8040

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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 present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors’ website. This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.

Bayesian Population Analysis Using WinBUGS

A Hierarchical Perspective

Author: Marc Kéry,Michael Schaub

Publisher: Academic Press

ISBN: 0123870208

Category: Science

Page: 535

View: 4224

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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

Hierarchical Modeling and Inference in Ecology

The Analysis of Data from Populations, Metapopulations and Communities

Author: J. Andrew Royle,Robert M. Dorazio

Publisher: Elsevier

ISBN: 0080559255

Category: Science

Page: 464

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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

Elements of Mathematical Ecology

Author: Mark Kot

Publisher: Cambridge University Press

ISBN: 1316584054

Category: Nature

Page: N.A

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Elements of Mathematical Ecology provides an introduction to classical and modern mathematical models, methods, and issues in population ecology. The first part of the book is devoted to simple, unstructured population models that ignore much of the variability found in natural populations for the sake of tractability. Topics covered include density dependence, bifurcations, demographic stochasticity, time delays, population interactions (predation, competition, and mutualism), and the application of optimal control theory to the management of renewable resources. The second part of this book is devoted to structured population models, covering spatially-structured population models (with a focus on reaction-diffusion models), age-structured models, and two-sex models. Suitable for upper level students and beginning researchers in ecology, mathematical biology and applied mathematics, the volume includes numerous clear line diagrams that clarify the mathematics, relevant problems thoughout the text that aid understanding, and supplementary mathematical and historical material that enrich the main text.

Animal Movement

Statistical Models for Telemetry Data

Author: Mevin B. Hooten,Devin S. Johnson,Brett T. McClintock,Juan M. Morales

Publisher: CRC Press

ISBN: 1466582154

Category: Mathematics

Page: 320

View: 2840

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The study of animal movement has always been a key element in ecological science, because it is inherently linked to critical processes that scale from individuals to populations and communities to ecosystems. Rapid improvements in biotelemetry data collection and processing technology have given rise to a variety of statistical methods for characterizing animal movement. The book serves as a comprehensive reference for the types of statistical models used to study individual-based animal movement. Animal Movement is an essential reference for wildlife biologists, quantitative ecologists, and statisticians who seek a deeper understanding of modern animal movement models. A wide variety of modeling approaches are reconciled in the book using a consistent notation. Models are organized into groups based on how they treat the underlying spatio-temporal process of movement. Connections among approaches are highlighted to allow the reader to form a broader view of animal movement analysis and its associations with traditional spatial and temporal statistical modeling. After an initial overview examining the role that animal movement plays in ecology, a primer on spatial and temporal statistics provides a solid foundation for the remainder of the book. Each subsequent chapter outlines a fundamental type of statistical model utilized in the contemporary analysis of telemetry data for animal movement inference. Descriptions begin with basic traditional forms and sequentially build up to general classes of models in each category. Important background and technical details for each class of model are provided, including spatial point process models, discrete-time dynamic models, and continuous-time stochastic process models. The book also covers the essential elements for how to accommodate multiple sources of uncertainty, such as location error and latent behavior states. In addition to thorough descriptions of animal movement models, differences and connections are also emphasized to provide a broader perspective of approaches.

Bayesian Model Selection and Statistical Modeling

Author: Tomohiro Ando

Publisher: CRC Press

ISBN: 9781439836156

Category: Mathematics

Page: 300

View: 2494

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Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model selection procedures. It thoroughly explains the concepts, illustrates the derivations of various Bayesian model selection criteria through examples, and provides R code for implementation. The author shows how to implement a variety of Bayesian inference using R and sampling methods, such as Markov chain Monte Carlo. He covers the different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors, the Bayesian predictive information criterion, and the deviance information criterion. He also provides a theoretical basis for the analysis of these criteria. In addition, the author discusses how Bayesian model averaging can simultaneously treat both model and parameter uncertainties. Selecting and constructing the appropriate statistical model significantly affect the quality of results in decision making, forecasting, stochastic structure explorations, and other problems. Helping you choose the right Bayesian model, this book focuses on the framework for Bayesian model selection and includes practical examples of model selection criteria.

A Critique for Ecology

Author: Robert Henry Peters

Publisher: Cambridge University Press

ISBN: 9780521395885

Category: Nature

Page: 366

View: 433

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This is a book of criticism. It is directed at contemporary ecology, but would apply to any science or indeed any statement that claims to contain information. Application of simple criteria to judge the information in ecological statements reveals deep inadequacies in the science. Furthermore, the complexity of the contemporary field of ecology and the mistraining of a generation of ecologists has obscured its weakness. As a result, many ecologists are unaware of the failings of the science although others are deeply concerned for the future of the field. The author, Professor Peters, argues that a return to simple question of fact, to observations, and to questions of general relevance to science and society can make ecology a useful, practical and informative science. Such science is desperately needed to meet the problems of the age. A thought-provoking book that will be of interest to all scientists, but in particular ecologists from undergraduates to senior academics and professionals.

Handbook of Meta-analysis in Ecology and Evolution

Author: Julia Koricheva,Jessica Gurevitch,Kerrie Mengersen

Publisher: Princeton University Press

ISBN: 1400846188

Category: Science

Page: 520

View: 8011

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Meta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies. This is the first comprehensive handbook of meta-analysis written specifically for ecologists and evolutionary biologists, and it provides an invaluable introduction for beginners as well as an up-to-date guide for experienced meta-analysts. The chapters, written by renowned experts, walk readers through every step of meta-analysis, from problem formulation to the presentation of the results. The handbook identifies both the advantages of using meta-analysis for research synthesis and the potential pitfalls and limitations of meta-analysis (including when it should not be used). Different approaches to carrying out a meta-analysis are described, and include moment and least-square, maximum likelihood, and Bayesian approaches, all illustrated using worked examples based on real biological datasets. This one-of-a-kind resource is uniquely tailored to the biological sciences, and will provide an invaluable text for practitioners from graduate students and senior scientists to policymakers in conservation and environmental management. Walks you through every step of carrying out a meta-analysis in ecology and evolutionary biology, from problem formulation to result presentation Brings together experts from a broad range of fields Shows how to avoid, minimize, or resolve pitfalls such as missing data, publication bias, varying data quality, nonindependence of observations, and phylogenetic dependencies among species Helps you choose the right software Draws on numerous examples based on real biological datasets

Handbook of Spatial Point-Pattern Analysis in Ecology

Author: Thorsten Wiegand,Kirk A. Moloney

Publisher: CRC Press

ISBN: 1420082558

Category: Mathematics

Page: 538

View: 6815

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Understand How to Analyze and Interpret Information in Ecological Point Patterns Although numerous statistical methods for analyzing spatial point patterns have been available for several decades, they haven’t been extensively applied in an ecological context. Addressing this gap, Handbook of Spatial Point-Pattern Analysis in Ecology shows how the techniques of point-pattern analysis are useful for tackling ecological problems. Within an ecological framework, the book guides readers through a variety of methods for different data types and aids in the interpretation of the results obtained by point-pattern analysis. Ideal for empirical ecologists who want to avoid advanced theoretical literature, the book covers statistical techniques for analyzing and interpreting the information contained in ecological patterns. It presents methods used to extract information hidden in spatial point-pattern data that may point to the underlying processes. The authors focus on point processes and null models that have proven their immediate utility for broad ecological applications, such as cluster processes. Along with the techniques, the handbook provides a comprehensive selection of real-world examples. Most of the examples are analyzed using Programita, a continuously updated software package based on the authors’ many years of teaching and collaborative research in ecological point-pattern analysis. Programita is tailored to meet the needs of real-world applications in ecology. The software and a manual are available online.

Ecological Forecasting

Author: Michael C. Dietze

Publisher: Princeton University Press

ISBN: 1400885450

Category: Science

Page: 288

View: 5465

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An authoritative and accessible introduction to the concepts and tools needed to make ecology a more predictive science Ecologists are being asked to respond to unprecedented environmental challenges. How can they provide the best available scientific information about what will happen in the future? Ecological Forecasting is the first book to bring together the concepts and tools needed to make ecology a more predictive science. Ecological Forecasting presents a new way of doing ecology. A closer connection between data and models can help us to project our current understanding of ecological processes into new places and times. This accessible and comprehensive book covers a wealth of topics, including Bayesian calibration and the complexities of real-world data; uncertainty quantification, partitioning, propagation, and analysis; feedbacks from models to measurements; state-space models and data fusion; iterative forecasting and the forecast cycle; and decision support. Features case studies that highlight the advances and opportunities in forecasting across a range of ecological subdisciplines, such as epidemiology, fisheries, endangered species, biodiversity, and the carbon cycle Presents a probabilistic approach to prediction and iteratively updating forecasts based on new data Describes statistical and informatics tools for bringing models and data together, with emphasis on: Quantifying and partitioning uncertainties Dealing with the complexities of real-world data Feedbacks to identifying data needs, improving models, and decision support Numerous hands-on activities in R available online

Occupancy Estimation and Modeling

Inferring Patterns and Dynamics of Species Occurrence

Author: Darryl I. MacKenzie,James D. Nichols,J. Andrew Royle,Kenneth H. Pollock,Larissa Bailey,James E. Hines

Publisher: Elsevier

ISBN: 0124072453

Category: Science

Page: 648

View: 1701

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Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition, provides a synthesis of model-based approaches for analyzing presence-absence data, allowing for imperfect detection. Beginning from the relatively simple case of estimating the proportion of area or sampling units occupied at the time of surveying, the authors describe a wide variety of extensions that have been developed since the early 2000s. This provides an improved insight about species and community ecology, including, detection heterogeneity; correlated detections; spatial autocorrelation; multiple states or classes of occupancy; changes in occupancy over time; species co-occurrence; community-level modeling, and more. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition has been greatly expanded and detail is provided regarding the estimation methods and examples of their application are given. Important study design recommendations are also covered to give a well rounded view of modeling. Provides authoritative insights into the latest in occupancy modeling Examines the latest methods in analyzing detection/no detection data surveys Addresses critical issues of imperfect detectability and its effects on species occurrence estimation Discusses important study design considerations such as defining sample units, sample size determination and optimal effort allocation

Bayesian Inference

With Ecological Applications

Author: William A Link,Richard J Barker

Publisher: Academic Press

ISBN: 0080889808

Category: Science

Page: 354

View: 1797

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This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analytical software and examples Leading authors with world-class reputations in ecology and biostatistics

Statistics for Ecologists Using R and Excel

Data Collection, Exploration, Analysis and Presentation

Author: Mark Gardener

Publisher: Pelagic Publishing Ltd

ISBN: 1784271411

Category: Science

Page: 406

View: 3801

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This is a book about the scientific process and how you apply it to data in ecology. You will learn how to plan for data collection, how to assemble data, how to analyze data and finally how to present the results. The book uses Microsoft Excel and the powerful Open Source R program to carry out data handling as well as producing graphs.Statistical approaches covered include: data exploration; tests for difference – t-test and U-test; correlation – Spearman’s rank test and Pearson product-moment; association including Chi-squared tests and goodness of fit; multivariate testing using analysis of variance (ANOVA) and Kruskal–Wallis test; and multiple regression.Key skills taught in this book include: how to plan ecological projects; how to record and assemble your data; how to use R and Excel for data analysis and graphs; how to carry out a wide range of statistical analyses including analysis of variance and regression; how to create professional looking graphs; and how to present your results.New in this edition: a completely revised chapter on graphics including graph types and their uses, Excel Chart Tools, R graphics commands and producing different chart types in Excel and in R; an expanded range of support material online, including; example data, exercises and additional notes & explanations; a new chapter on basic community statistics, biodiversity and similarity; chapter summaries and end-of-chapter exercises.Praise for the first edition:This book is a superb way in for all those looking at how to design investigations and collect data to support their findings. – Sue Townsend, Biodiversity Learning Manager, Field Studies Council[M]akes it easy for the reader to synthesise R and Excel and there is extra help and sample data available on the free companion webpage if needed. I recommended this text to the university library as well as to colleagues at my student workshops on R. Although I initially bought this book when I wanted to discover R I actually also learned new techniques for data manipulation and management in Excel – Mark Edwards, EcoBloggingA must for anyone getting to grips with data analysis using R and excel. – Amazon 5-star reviewIt has been very easy to follow and will be perfect for anyone. – Amazon 5-star reviewA solid introduction to working with Excel and R. The writing is clear and informative, the book provides plenty of examples and figures so that each string of code in R or step in Excel is understood by the reader. – Goodreads, 4-star review

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

Author: Franzi Korner-Nievergelt,Tobias Roth,Stefanie von Felten,Jérôme Guélat,Bettina Almasi,Pius Korner-Nievergelt

Publisher: Academic Press

ISBN: 0128016787

Category: Science

Page: 328

View: 4853

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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

Urban Ecology

Science of Cities

Author: Richard T. T. Forman

Publisher: Cambridge University Press

ISBN: 1107007003

Category: Nature

Page: 478

View: 9200

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The first richly illustrated worldwide portrayal of urban ecology, tying together organisms, built structures, and the physical environment around cities.