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WAIC and WBIC with Python Stan

Author: Joe Suzuki

Publisher: Springer Nature

Published: 2024-01-09

Total Pages: 249

ISBN-13: 9819938414

Master the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. The book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in Python and Stan. Whether you’re a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory. The key features of this indispensable book include: A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise. 100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension. A comprehensive guide to Sumio Watanabe’s groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians. Detailed source programs and Stan codes that will enhance readers’ grasp of the mathematical concepts presented. A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting. Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!

WAIC and WBIC with R Stan

Author: Joe Suzuki

Publisher: Springer Nature

Published: 2023-11-25

Total Pages: 241

ISBN-13: 9819938384

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Master the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. This book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in R and Stan. Whether you’re a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory. The key features of this indispensable book include: A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise. 100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension. A comprehensive guide to Sumio Watanabe’s groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians. Detailed source programs and Stan codes that will enhance readers’ grasp of the mathematical concepts presented. A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting. Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!

Bayesian Statistical Modeling with Stan, R, and Python

Author: Kentaro Matsuura

Publisher: Springer Nature

Published: 2023-01-24

Total Pages: 395

ISBN-13: 9811947554

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This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language. The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very beginning to basic regression analyses. The third part then introduces a number of probability distributions, nonlinear models, and hierarchical (multilevel) models, which are essential to mastering statistical modeling. It also describes a wide range of frequently used modeling techniques, such as censoring, outliers, missing data, speed-up, and parameter constraints, and discusses how to lead convergence of MCMC. Lastly, the fourth part examines advanced topics for real-world data: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 lines. Using numerous easy-to-understand examples, the book explains key concepts, which continue to be useful when using future versions of Stan and when using other statistical modeling tools. The examples do not require domain knowledge and can be generalized to many fields. The book presents full explanations of code and math formulas, enabling readers to extend models for their own problems. All the code and data are on GitHub.

Advancements in Bayesian Methods and Implementations

Author:

Publisher: Academic Press

Published: 2022-10-06

Total Pages: 322

ISBN-13: 0323952690

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Advancements in Bayesian Methods and Implementation, Volume 47 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Fisher Information, Cramer-Rao and Bayesian Paradigm, Compound beta binomial distribution functions, MCMC for GLMMS, Signal Processing and Bayesian, Mathematical theory of Bayesian statistics where all models are wrong, Machine Learning and Bayesian, Non-parametric Bayes, Bayesian testing, and Data Analysis with humans, Variational inference or Functional horseshoe, Generalized Bayes. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Statistics series Updated release includes the latest information on Advancements in Bayesian Methods and Implementation

Bayesian Data Analysis, Third Edition

Author: Andrew Gelman

Publisher: CRC Press

Published: 2013-11-01

Total Pages: 677

ISBN-13: 1439840954

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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.

ASP.NET Core and Vue.js

Author: Devlin Basilan Duldulao

Publisher: Packt Publishing Ltd

Published: 2021-06-16

Total Pages: 479

ISBN-13: 1800201265

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A busy .NET developer's step-by-step guide to building fully functional, cloud-ready, and professional web apps without diving into the theory of frameworks and libraries Key FeaturesDiscover tenants of clean architecture in the latest ASP.NET Core 5 Web APIDevelop Vue.js 3 single-page applications (SPAs) using TypeScript and VuexLearn techniques to secure, test, and deploy your full-stack web apps on AzureBook Description Vue.js 3 is faster and smaller than the previous version, and TypeScript’s full support out of the box makes it a more maintainable and easier-to-use version of Vue.js. Then, there's ASP.NET Core 5, which is the fastest .NET web framework today. Together, Vue.js for the frontend and ASP.NET Core 5 for the backend make a powerful combination. This book follows a hands-on approach to implementing practical methodologies for building robust applications using ASP.NET Core 5 and Vue.js 3. The topics here are not deep dive and the book is intended for busy .NET developers who have limited time and want a quick implementation of a clean architecture with popular libraries. You’ll start by setting up your web app’s backend, guided by clean architecture, command query responsibility segregation (CQRS), mediator pattern, and Entity Framework Core 5. The book then shows you how to build the frontend application using best practices, state management with Vuex, Vuetify UI component libraries, Vuelidate for input validations, lazy loading with Vue Router, and JWT authentication. Later, you’ll focus on testing and deployment. All the tutorials in this book support Windows 10, macOS, and Linux users. By the end of this book, you’ll be able to build an enterprise full-stack web app, use the most common npm packages for Vue.js and NuGet packages for ASP.NET Core, and deploy Vue.js and ASP.NET Core to Azure App Service using GitHub Actions. What you will learnDiscover CQRS and mediator pattern in the ASP.NET Core 5 Web APIUse Serilog, MediatR, FluentValidation, and Redis in ASP.NETExplore common Vue.js packages such as Vuelidate, Vuetify, and VuexManage complex app states using the Vuex state management libraryWrite integration tests in ASP.NET Core using xUnit and FluentAssertionsDeploy your app to Microsoft Azure using the new GitHub Actions for continuous integration and continuous deployment (CI/CD)Who this book is for This app development book is for .NET developers who want to get started with Vue.js and build full-stack enterprise web applications. Web developers looking to build a proof-of-concept application quickly and pragmatically using their existing knowledge of ASP.NET Core as well as developers who want to write readable and maintainable code using TypeScript and the C# programming language will also find this book useful. The book assumes intermediate-level .NET knowledge along with an understanding of C# programming, JavaScript, and ECMAScript.

Introduction to Algorithms, fourth edition

Author: Thomas H. Cormen

Publisher: MIT Press

Published: 2022-04-05

Total Pages: 1313

ISBN-13: 026204630X

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A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout. New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learning New material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays 140 new exercises and 22 new problems Reader feedback–informed improvements to old problems Clearer, more personal, and gender-neutral writing style Color added to improve visual presentation Notes, bibliography, and index updated to reflect developments in the field Website with new supplementary material Warning: Avoid counterfeit copies of Introduction to Algorithms by buying only from reputable retailers. Counterfeit and pirated copies are incomplete and contain errors.

Mathematical Theory of Bayesian Statistics

Author: Sumio Watanabe

Publisher: CRC Press

Published: 2018-04-27

Total Pages: 331

ISBN-13: 148223808X

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Mathematical Theory of Bayesian Statistics introduces the mathematical foundation of Bayesian inference which is well-known to be more accurate in many real-world problems than the maximum likelihood method. Recent research has uncovered several mathematical laws in Bayesian statistics, by which both the generalization loss and the marginal likelihood are estimated even if the posterior distribution cannot be approximated by any normal distribution. Features Explains Bayesian inference not subjectively but objectively. Provides a mathematical framework for conventional Bayesian theorems. Introduces and proves new theorems. Cross validation and information criteria of Bayesian statistics are studied from the mathematical point of view. Illustrates applications to several statistical problems, for example, model selection, hyperparameter optimization, and hypothesis tests. This book provides basic introductions for students, researchers, and users of Bayesian statistics, as well as applied mathematicians. Author Sumio Watanabe is a professor of Department of Mathematical and Computing Science at Tokyo Institute of Technology. He studies the relationship between algebraic geometry and mathematical statistics.

Bayesian Population Analysis Using WinBUGS

Author: Marc Kéry

Publisher: Academic Press

Published: 2012

Total Pages: 556

ISBN-13: 0123870208

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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

Handbook of Markov Chain Monte Carlo

Author: Steve Brooks

Publisher: CRC Press

Published: 2011-05-10

Total Pages: 620

ISBN-13: 1420079425

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Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisherie