Bringing Bayesian Models To Life

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Bringing Bayesian Models to Life

Mevin B. Hooten,Trevor Hefley
Bringing Bayesian Models to Life Book PDF

Author : Mevin B. Hooten,Trevor Hefley
Publisher : CRC Press
Page : 591 pages
File Size : 48,8 Mb
Release : 2019-05-15
Category : Science
ISBN : 9780429513374

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Bringing Bayesian Models to Life by Mevin B. Hooten,Trevor Hefley Pdf

Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental data analysis. We open the black box and show the reader how to connect modern statistical models to computer algorithms. These algorithms allow the user to fit models that answer their scientific questions without needing to rely on automated Bayesian software. We show how to handcraft statistical models that are useful in ecological and environmental science including: linear and generalized linear models, spatial and time series models, occupancy and capture-recapture models, animal movement models, spatio-temporal models, and integrated population-models. Features: R code implementing algorithms to fit Bayesian models using real and simulated data examples. A comprehensive review of statistical models commonly used in ecological and environmental science. Overview of Bayesian computational methods such as importance sampling, MCMC, and HMC. Derivations of the necessary components to construct statistical algorithms from scratch. Bringing Bayesian Models to Life contains a comprehensive treatment of models and associated algorithms for fitting the models to data. We provide detailed and annotated R code in each chapter and apply it to fit each model we present to either real or simulated data for instructional purposes. Our code shows how to create every result and figure in the book so that readers can use and modify it for their own analyses. We provide all code and data in an organized set of directories available at the authors' websites.

Bayesian Models

N. Thompson Hobbs,Mevin Hooten
Bayesian Models Book PDF

Author : N. Thompson Hobbs,Mevin Hooten
Publisher : Princeton University Press
Page : 315 pages
File Size : 42,8 Mb
Release : 2015-08-04
Category : Science
ISBN : 9781400866557

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Bayesian Models by N. Thompson Hobbs,Mevin Hooten Pdf

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

Bayes Rules!

Alicia A. Johnson,Miles Q. Ott,Mine Dogucu
Bayes Rules Book PDF

Author : Alicia A. Johnson,Miles Q. Ott,Mine Dogucu
Publisher : CRC Press
Page : 543 pages
File Size : 48,6 Mb
Release : 2022-03-03
Category : Mathematics
ISBN : 9781000529500

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Bayes Rules! by Alicia A. Johnson,Miles Q. Ott,Mine Dogucu Pdf

Praise for Bayes Rules!: An Introduction to Applied Bayesian Modeling “A thoughtful and entertaining book, and a great way to get started with Bayesian analysis.” Andrew Gelman, Columbia University “The examples are modern, and even many frequentist intro books ignore important topics (like the great p-value debate) that the authors address. The focus on simulation for understanding is excellent.” Amy Herring, Duke University “I sincerely believe that a generation of students will cite this book as inspiration for their use of – and love for – Bayesian statistics. The narrative holds the reader’s attention and flows naturally – almost conversationally. Put simply, this is perhaps the most engaging introductory statistics textbook I have ever read. [It] is a natural choice for an introductory undergraduate course in applied Bayesian statistics." Yue Jiang, Duke University “This is by far the best book I’ve seen on how to (and how to teach students to) do Bayesian modeling and understand the underlying mathematics and computation. The authors build intuition and scaffold ideas expertly, using interesting real case studies, insightful graphics, and clear explanations. The scope of this book is vast – from basic building blocks to hierarchical modeling, but the authors’ thoughtful organization allows the reader to navigate this journey smoothly. And impressively, by the end of the book, one can run sophisticated Bayesian models and actually understand the whys, whats, and hows.” Paul Roback, St. Olaf College “The authors provide a compelling, integrated, accessible, and non-religious introduction to statistical modeling using a Bayesian approach. They outline a principled approach that features computational implementations and model assessment with ethical implications interwoven throughout. Students and instructors will find the conceptual and computational exercises to be fresh and engaging.” Nicholas Horton, Amherst College An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable, hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum. Features • Utilizes data-driven examples and exercises. • Emphasizes the iterative model building and evaluation process. • Surveys an interconnected range of multivariable regression and classification models. • Presents fundamental Markov chain Monte Carlo simulation. • Integrates R code, including RStan modeling tools and the bayesrules package. • Encourages readers to tap into their intuition and learn by doing. • Provides a friendly and inclusive introduction to technical Bayesian concepts. • Supports Bayesian applications with foundational Bayesian theory.

Statistical Modeling With R

Pablo Inchausti
Statistical Modeling With R Book PDF

Author : Pablo Inchausti
Publisher : Oxford University Press
Page : 519 pages
File Size : 50,8 Mb
Release : 2022-11-02
Category : Science
ISBN : 9780192675033

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Statistical Modeling With R by Pablo Inchausti Pdf

To date, statistics has tended to be neatly divided into two theoretical approaches or frameworks: frequentist (or classical) and Bayesian. Scientists typically choose the statistical framework to analyse their data depending on the nature and complexity of the problem, and based on their personal views and prior training on probability and uncertainty. Although textbooks and courses should reflect and anticipate this dual reality, they rarely do so. This accessible textbook explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists. It presents the material in an informal, approachable, and progressive manner suitable for readers with only a basic knowledge of calculus and statistics. Statistical Modeling with R is aimed at senior undergraduate and graduate students, professional researchers, and practitioners throughout the life sciences, seeking to strengthen their understanding of quantitative methods and to apply them successfully to real world scenarios, whether in the fields of ecology, evolution, environmental studies, or computational biology.

Bayesian Inference of State Space Models

Kostas Triantafyllopoulos
Bayesian Inference of State Space Models Book PDF

Author : Kostas Triantafyllopoulos
Publisher : Springer Nature
Page : 503 pages
File Size : 43,5 Mb
Release : 2021-11-12
Category : Mathematics
ISBN : 9783030761240

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Bayesian Inference of State Space Models by Kostas Triantafyllopoulos Pdf

Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models. The celebrated Kalman filter, with its numerous extensions, takes centre stage in the book. Univariate and multivariate models, linear Gaussian, non-linear and non-Gaussian models are discussed with applications to signal processing, environmetrics, economics and systems engineering. Over the past years there has been a growing literature on Bayesian inference of state space models, focusing on multivariate models as well as on non-linear and non-Gaussian models. The availability of time series data in many fields of science and industry on the one hand, and the development of low-cost computational capabilities on the other, have resulted in a wealth of statistical methods aimed at parameter estimation and forecasting. This book brings together many of these methods, presenting an accessible and comprehensive introduction to state space models. A number of data sets from different disciplines are used to illustrate the methods and show how they are applied in practice. The R package BTSA, created for the book, includes many of the algorithms and examples presented. The book is essentially self-contained and includes a chapter summarising the prerequisites in undergraduate linear algebra, probability and statistics. An up-to-date and complete account of state space methods, illustrated by real-life data sets and R code, this textbook will appeal to a wide range of students and scientists, notably in the disciplines of statistics, systems engineering, signal processing, data science, finance and econometrics. With numerous exercises in each chapter, and prerequisite knowledge conveniently recalled, it is suitable for upper undergraduate and graduate courses.

Bayesian Time Series Models

David Barber,A. Taylan Cemgil,Silvia Chiappa
Bayesian Time Series Models Book PDF

Author : David Barber,A. Taylan Cemgil,Silvia Chiappa
Publisher : Cambridge University Press
Page : 432 pages
File Size : 43,9 Mb
Release : 2011-08-11
Category : Computers
ISBN : 9780521196765

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Bayesian Time Series Models by David Barber,A. Taylan Cemgil,Silvia Chiappa Pdf

The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.

Bayes Rules!

Alicia A. Johnson,Miles Q. Ott,Mine Dogucu
Bayes Rules Book PDF

Author : Alicia A. Johnson,Miles Q. Ott,Mine Dogucu
Publisher : CRC Press
Page : 606 pages
File Size : 41,7 Mb
Release : 2022-03-03
Category : Mathematics
ISBN : 9781000529562

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Bayes Rules! by Alicia A. Johnson,Miles Q. Ott,Mine Dogucu Pdf

Praise for Bayes Rules!: An Introduction to Applied Bayesian Modeling “A thoughtful and entertaining book, and a great way to get started with Bayesian analysis.” Andrew Gelman, Columbia University “The examples are modern, and even many frequentist intro books ignore important topics (like the great p-value debate) that the authors address. The focus on simulation for understanding is excellent.” Amy Herring, Duke University “I sincerely believe that a generation of students will cite this book as inspiration for their use of – and love for – Bayesian statistics. The narrative holds the reader’s attention and flows naturally – almost conversationally. Put simply, this is perhaps the most engaging introductory statistics textbook I have ever read. [It] is a natural choice for an introductory undergraduate course in applied Bayesian statistics." Yue Jiang, Duke University “This is by far the best book I’ve seen on how to (and how to teach students to) do Bayesian modeling and understand the underlying mathematics and computation. The authors build intuition and scaffold ideas expertly, using interesting real case studies, insightful graphics, and clear explanations. The scope of this book is vast – from basic building blocks to hierarchical modeling, but the authors’ thoughtful organization allows the reader to navigate this journey smoothly. And impressively, by the end of the book, one can run sophisticated Bayesian models and actually understand the whys, whats, and hows.” Paul Roback, St. Olaf College “The authors provide a compelling, integrated, accessible, and non-religious introduction to statistical modeling using a Bayesian approach. They outline a principled approach that features computational implementations and model assessment with ethical implications interwoven throughout. Students and instructors will find the conceptual and computational exercises to be fresh and engaging.” Nicholas Horton, Amherst College An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable, hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum. Features • Utilizes data-driven examples and exercises. • Emphasizes the iterative model building and evaluation process. • Surveys an interconnected range of multivariable regression and classification models. • Presents fundamental Markov chain Monte Carlo simulation. • Integrates R code, including RStan modeling tools and the bayesrules package. • Encourages readers to tap into their intuition and learn by doing. • Provides a friendly and inclusive introduction to technical Bayesian concepts. • Supports Bayesian applications with foundational Bayesian theory.

Bayesian Data Analysis

Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Bayesian Data Analysis Book PDF

Author : Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Publisher : CRC Press
Page : 663 pages
File Size : 52,9 Mb
Release : 2013-11-27
Category : Mathematics
ISBN : 9781439898208

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Bayesian Data Analysis by Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin Pdf

Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow 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

Statistical Rethinking

Richard McElreath
Statistical Rethinking Book PDF

Author : Richard McElreath
Publisher : CRC Press
Page : 488 pages
File Size : 47,7 Mb
Release : 2018-01-03
Category : Mathematics
ISBN : 9781315362618

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Statistical Rethinking by Richard McElreath Pdf

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

Bayesian Models for Categorical Data

Peter Congdon
Bayesian Models for Categorical Data Book PDF

Author : Peter Congdon
Publisher : John Wiley & Sons
Page : 446 pages
File Size : 54,6 Mb
Release : 2005-12-13
Category : Mathematics
ISBN : 9780470092385

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Bayesian Models for Categorical Data by Peter Congdon Pdf

The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes. * Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data). * Considers missing data models techniques and non-standard models (ZIP and negative binomial). * Evaluates time series and spatio-temporal models for discrete data. * Features discussion of univariate and multivariate techniques. * Provides a set of downloadable worked examples with documented WinBUGS code, available from an ftp site. The author's previous 2 bestselling titles provided a comprehensive introduction to the theory and application of Bayesian models. Bayesian Models for Categorical Data continues to build upon this foundation by developing their application to categorical, or discrete data - one of the most common types of data available. The author's clear and logical approach makes the book accessible to a wide range of students and practitioners, including those dealing with categorical data in medicine, sociology, psychology and epidemiology.

Integrated Population Models

Michael Schaub,Marc Kéry
Integrated Population Models Book PDF

Author : Michael Schaub,Marc Kéry
Publisher : Academic Press
Page : 640 pages
File Size : 43,7 Mb
Release : 2021-11-12
Category : Science
ISBN : 9780128209158

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Integrated Population Models by Michael Schaub,Marc Kéry Pdf

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

Statistical Modeling with R

Pablo Inchausti
Statistical Modeling with R Book PDF

Author : Pablo Inchausti
Publisher : Oxford University Press
Page : 519 pages
File Size : 41,8 Mb
Release : 2023-01-11
Category : Mathematics
ISBN : 9780192859013

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Statistical Modeling with R by Pablo Inchausti Pdf

To date, statistics has tended to be neatly divided into two theoretical approaches or frameworks: frequentist (or classical) and Bayesian. Scientists typically choose the statistical framework to analyse their data depending on the nature and complexity of the problem, and based on their personal views and prior training on probability and uncertainty. Although textbooks and courses should reflect and anticipate this dual reality, they rarely do so. This accessible textbook explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists. It presents the material in an informal, approachable, and progressive manner suitable for readers with only a basic knowledge of calculus and statistics. Statistical Modeling with R is aimed at senior undergraduate and graduate students, professional researchers, and practitioners throughout the life sciences, seeking to strengthen their understanding of quantitative methods and to apply them successfully to real world scenarios, whether in the fields of ecology, evolution, environmental studies, or computational biology.

Cybersecurity and Resilience in the Arctic

B.D. Trump,K. Hossain,I. Linkov
Cybersecurity and Resilience in the Arctic Book PDF

Author : B.D. Trump,K. Hossain,I. Linkov
Publisher : IOS Press
Page : 456 pages
File Size : 49,7 Mb
Release : 2020-07-24
Category : Computers
ISBN : 9781643680774

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Cybersecurity and Resilience in the Arctic by B.D. Trump,K. Hossain,I. Linkov Pdf

Until recently, the Arctic was almost impossible for anyone other than indigenous peoples and explorers to traverse. Pervasive Arctic sea ice and harsh climatological conditions meant that the region was deemed incapable of supporting industrial activity or a Western lifestyle. In the last decade, however, that longstanding reality has been dramatically and permanently altered. Receding sea ice, coupled with growing geopolitical disputes over Arctic resources, territory, and transportation channels, has stimulated efforts to exploit newly-open waterways, to identify and extract desirable resources, and to leverage industrial, commercial, and transportation opportunities emerging throughout the region. This book presents papers from the NATO Advanced Research Workshop (ARW) Governance for Cyber Security and Resilience in the Arctic. Held in Rovaniemi, Finland, from 27-30 January 2019, the workshop brought together top scholars in cybersecurity risk assessment, governance, and resilience to discuss potential analytical and governing strategies and offer perspectives on how to improve critical Arctic infrastructure against various human and natural threats. The book is organized in three sections according to topical group and plenary discussions at the meeting on: cybersecurity infrastructure and threats, analytical strategies for infrastructure threat absorption and resilience, and legal frameworks and governance options to promote cyber resilience. Summaries and detailed analysis are included within each section as summary chapters in the book. The book provides a background on analytical tools relevant to risk and resilience analytics, including risk assessment, decision analysis, supply chain management and resilience analytics. It will allow government, native and civil society groups, military stakeholders, and civilian practitioners to understand better on how to enhance the Arctic’s resilience against various natural and anthropogenic challenges.

Introduction to Hierarchical Bayesian Modeling for Ecological Data

Eric Parent,Etienne Rivot
Introduction to Hierarchical Bayesian Modeling for Ecological Data Book PDF

Author : Eric Parent,Etienne Rivot
Publisher : CRC Press
Page : 429 pages
File Size : 53,6 Mb
Release : 2012-08-21
Category : Mathematics
ISBN : 9781584889199

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Introduction to Hierarchical Bayesian Modeling for Ecological Data by Eric Parent,Etienne Rivot Pdf

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 Essentials with R

Jean-Michel Marin,Christian P. Robert
Bayesian Essentials with R Book PDF

Author : Jean-Michel Marin,Christian P. Robert
Publisher : Springer Science & Business Media
Page : 305 pages
File Size : 43,9 Mb
Release : 2013-10-28
Category : Computers
ISBN : 9781461486879

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Bayesian Essentials with R by Jean-Michel Marin,Christian P. Robert Pdf

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics.