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Gelman Andrew: Bayesian Workflow

Bayesian Workflow

This publication addresses copula approaches to competing risks models for dependent latent failure times. It will establish the fundamentals on dependent competing risks models, as well as a range of recent developments to identifiability and estimation using copulas. Readers will learn from the presentation of theory, in relation to its applications in different subject areas in economics, engineering, and medicine. The comprehensive knowledge provided in this book gives readers an overview of existing models for dependent competing risks, which are not found in other books.The book covers basic statistical ideas, such as "parametric latent failure time models", theoretical issues, such as "identifiability of the competing risks model", and model comparisons, such as comparative studies for "different copulas and different models for hazard functions". Aside from the introductory materials, the authors cover new and recently developed methodologies. The new solutions address the identifiability problems in competing risks models that rely on copula modelling. Copula models and other competing risks models are presented in unified theoretical framework and illustrated with data from various disciplines (such as economics, engineering, and medicine). The book also includes applications with real data and the code (written by R and/or Stata) will be made available for easy practice purposes.A selection of exercises and their solutions will be available online. This book serves as a good reading for senior undergraduates and postgraduate students studying in such courses as "Multivariate Survival Analysis", "Survival Data Analysis" or "Advanced Econometrics". This book would also serve as a useful reference for empirical researchers in a wide range of disciplines.

Theory and Applications in Economics, Engineering, Medicine

Copula Models for Dependent Competing Risks

Cornerstones in Quantitative Empirical Methods - Volume I: Foundations provides a complete, self-contained path from first principles to modern statistical inference, giving a comprehensive technical foundation for understanding and analyzing data problems using quantitative methods. The first part of the book provides a thorough introduction to probability theory and statistics with a focus on techniques and concepts, which are particularly useful for empirical analyses. Building directly on the first part, the second part of the book provides an introduction to estimation theory, hypothesis testing, likelihood theory, and Bayesian methods.The nineteen concise chapters follow a transparent pattern: motivation and intuition, carefully chosen examples, main results stated as formal theorems, and a curated set of exercises that reinforce both computation and reasoning. Short illustrative datasets keep the exposition concrete while maintaining the book's primary focus on theory. Three appendices consolidate the required background in set theory, real analysis, and simulation techniques, making the volume fully self-contained.Designed for graduate and advanced-undergraduate courses in e.g. economics, political science, business analytics, or data science-or for motivated self-study-Volume I equips readers with the concepts and techniques needed for quantitative empirical analysis. It also prepares readers for Volume II, which builds the foundation for modelling, prediction, and causal inference in empirical studies.Key Features:<ul> <li>Comprehensive coverage across the technical fields of probability theory, statistics, and mathematics, needed to understand and perform in-depth empirical modelling and analysis</li> <li>Extensive treatment of estimation theory and various estimation methods, such as analog estimators, maximum likelihood estimators and Bayesian methods</li> <li>Elaborate discussion of hypothesis testing and recommendations to avoid pitfalls</li> <li>Inclusion of technical details and proofs structured such that they can be skipped by readers who prefer a less technical, though still cohesive, approach</li> </ul>

Volume 1: Foundations

Cornerstones in Quantitative Empirical Methods

Accelerated life tests (ALTs) have become an indispensable tool for reliability engineers to quantify a product's reliability, and for product managers to make product release decisions. However, there are many common misuses of ALTs and misinterpretations of their results. This book provides a framework for the proper design and analysis of ALTs. It compares ALTs with other industrial testing experiments, and provides guidance on their appropriate application. It features lots of examples using real data from industrial experiments, and provides statistical software for their implementation. It can be used as a textbook for students of reliability, or as a reference for practitioners

Accelerated life tests (ALTs) have become an indispensable tool for reliability engineers to quantify a product’s reliability, and for product managers to make product release decisions. However, there are many common misuses of ALTs and misinterpretations of their results. This book provides a framework for the proper design and analysis of ALTs. It compares ALTs with other industrial testing experiments, and provides guidance on their appropriate application. It features lots of examples using real data from industrial experiments, and provides statistical software for their implementation. It can be used as a textbook for students of reliability, or as a reference for practitioners

Design and Analysis of Accelerated Life Tests

Chapman & Hall/CRC Texts in Statistical Science

Bayesian Statistical Methods: With Applications to Machine Learning provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, this book is more focused on Bayesian methods applied routinely in practice, including multiple linear regression, mixed effects models and generalized linear models. This second edition includes a new chapter on Bayesian machine learning methods to handle large and complex datasets and several new applications to illustrate the benefits of the Bayesian approach in terms of uncertainty quantification. Readers familiar with only introductory statistics will find this book accessible, as it includes many worked examples with complete R code, and comparisons are presented with analogous frequentist procedures. The book can be used as a one-semester course for advanced undergraduate and graduate students and can be used in courses comprising undergraduate statistics majors, as well as non-statistics graduate students from other disciplines such as engineering, ecology and psychology. In addition to thorough treatment of the basic concepts of Bayesian inferential methods, the book covers many general topics:<ul> <li>Advice on selecting prior distributions</li> <li>Computational methods including Markov chain Monte Carlo (MCMC) sampling</li> <li>Model-comparison and goodness-of-fit measures, including sensitivity to priors.</li> </ul>To illustrate the flexibility of the Bayesian approaches for complex data structures, the latter chapters provide case studies covering advanced topics:<ul> <li>Handling of missing and censored data</li> <li>Priors for high-dimensional regression models</li> <li>Machine learning models including Bayesian adaptive regression trees and deep learning</li> <li>Computational techniques for large datasets</li> <li>Frequentist properties of Bayesian methods.</li> </ul>The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets and complete data analyses is made available on the book's website.

With Applications to Machine Learning

Bayesian Statistical Methods

Shortlisted for the 2026 Unwin Award Polls show that over fifty per cent of us don't believe racism exists. In a world made for White people, The Science of Racism uses clear scientific research to expose what we know about racism, exactly how we know it, and what we can do about it. 'Charismatic and funny' - The Guardian 'Illuminating, surprising, unnerving, moving' - Sathnam Sanghera 'Short circuits the brain' - The Independent * British police are nine times more likely to use their tasers on Black people. * An item being advertised on eBay will sell for approximately twenty per cent less if a dark-skinned hand is shown in the photos. * Black Caribbean applicants have to apply for around fifty per cent more jobs in order to receive a positive response. Professor Keon West cuts through the divisive anecdotes and rhetoric with decades' worth of science - the data that reveals truths about racism that are shocking and tragic, but also (somehow) funny and entertaining. Whatever you've read before, this book will ensure that you never see racism in the same way again.

Everything you need to know but probably don't - yet

The Science of Racism

The book presents real-life applications of Data Science and Big Data for Sustainable Development Goals. It includes a list of case studies from different regions across India. The book uses both structured and unstructured data like numeric data, textual data, video/image data, etc., across the various chapters for the analysis. It further delves into various data science techniques, starting from data collection on the ground in the unavailability of data to a dashboard, reporting, unsupervised methods like clustering, artificial intelligence, machine learning and deep learning, search or information retrieval, time series forecasting, optimisation, etc.¿ Showcase data science decision-making processes, driving innovation, and solving complex problems in real-case scenarios from across sectors like finance, healthcare, and e-commerce.¿ The SDGs provide a framework for societal development and well-being for all; the data science and big data interventions in this book are aligned towards mapping the various SDG goals.¿ Most of the data science use cases and initiatives projects covered in this book have been implemented by the central or state governments across different states of India.¿ Shows how data science intervention can transform the social sector, potentially driving positive change and addressing critical societal challenges.¿ Explained the fundamentals of data science theories, including concepts like classification, classification, regression, predictive analytics, optimisation, artificial intelligence, deep learning, time series forecasting, etc. for readers of different disciplines.It serves as a valuable reference for graduate students, researchers, and scholars seeking to deepen their knowledge and engage with real life applications of Data Science. This will also serve as a valuable resource for government officers and policy practitioners, providing a range of cases on the use of data-based methods for improving governance and policy making.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC BY-NC-ND)] 4.0 license funded by by the Great Lakes Institute of Management, Gurgaon Campus, Delhi NCR, India.

Indian Case Studies

Data Science for Sustainable Development Goals

Statistical skills are essential for analysing data and ensuring optimal business operation and efficiency. In this clear and concise textbook, Les Oakshott and Kathy Kotiadis guide students through key quantitative techniques, from basic maths and statistics through to advanced topics such as hypothesis testing and linear programming. Grounded in the everyday realities of business, this textbook introduces each chapter with an engaging case study to highlight how organisations operationalise these techniques, ranging from the growth of China's economy and a Covid-19 infection survey to goal scoring at Euro 2024 and the effect of global warming on the occurrence of salmonella in Singapore. New to this edition: -Links the UN Sustainable Development Goals to quantitative techniques -Greater emphasis on Excel to enable seamless transition to blended learning approaches -Walk-through videos of key processes hosted on the companion website This is the essential textbook for delivering quantitative methods and business statistics modules to students of mixed maths abilities across undergraduate and postgraduate levels. Accompanying online resources for this title can be found at bloomsburypub/essential-quantitative-methods-8e. These resources are designed to support teaching and learning at no extra cost.

for Business, Management and Finance

Essential Quantitative Methods

This textbook for advanced undergraduate and graduate students provides a clear and practical guide to the principles and methods for structuring and solving complex problems that include multiple, often conflicting criteria.The book introduces the foundations of multi-criteria decision-making (MCDM) and its relationship with complementary approaches such as multi-objective optimization and goal programming. It then presents three major MCDM methods: reference-type methods such as TOPSIS, aggregation-type methods such as AHP, and outranking-type methods such as ELECTRE. Each method is explained with numerical examples, and strengths and limitations discussed. Preprocessing techniques are also covered. The book highlights user-friendly software tools, including Microsoft Excel-based programs and open-source Python libraries. End-of-chapter exercises are also provided to ensure that readers can consolidate their knowledge and apply this to real-world applications.This textbook is vital for advanced undergraduate and graduate students taking decision science and optimization courses in disciplines such as engineering, business, finance, and management. Its focus on recent methods and developments will ensure that students are well prepared for employment in their field or for further research alike.

Principles, Methods and Programs

Multi-Criteria Decision-Making

College Test Preparation

PREMIUM PREP FOR A PERFECT 5! Ace the hybrid digital AP Statistics Exam with this comprehensive study guide—including 5 full-length practice tests with answer explanations,&nbsp;timed online practice, and thorough content reviews. The Princeton Review's AP Statistics&nbsp;Premium Prep is a one-stop-shop guide that will help you prep for the exam with confidence. In this book, you'll get: The Princeton Review Expertise <ul><li>Study with prep and practice written entirely by AP educators</li><li>Learn test-taking strategies backed by 40+ years of test prep success&nbsp;</li><li>Focused, supportive lessons designed and perfected by experts</li></ul> Everything You Need for a High Score <ul><li>Comprehensive review of all topics on the new hybrid digital exam</li></ul><ul><li>A step-by-step guide on how to better your score with this book</li></ul><ul><li>A diagnostic key to customize a study plan that targets areas of improvement</li><li>Online digital flashcards to review core content</li></ul><ul><li>Access to study plans, a handy list of key terms and concepts, and more via your online Student Tools</li></ul> Premium Practice for AP Excellence <ul><li>5 full-length practice tests (2 in the book, 3 online) with detailed answer explanations</li><li>Online tests provided as two formats: digitally (with timer option to simulate exam experience) and as downloadable PDFs (with interactive elements mimicking the exam interface)</li><li>Practice drills in each content review chapter, plus end-of-chapter key term lists</li></ul>

PREMIUM PREP FOR A PERFECT 5! Ace the hybrid digital AP Statistics Exam with this comprehensive study guide—including 5 full-length practice tests with answer explanations,&nbsp;timed online practice, and thorough content reviews. The Princeton Review's AP Statistics&nbsp;Premium Prep is a one-stop-shop guide that will help you prep for the exam with confidence. In this book, you'll get: The Princeton Review Expertise <ul><li>Study with prep and practice written entirely by AP educators</li><li>Learn test-taking strategies backed by 40+ years of test prep success&nbsp;</li><li>Focused, supportive lessons designed and perfected by experts</li></ul>Everything You Need for a High Score <ul><li>A step-by-step guide on how to better your score with this book</li><li>Comprehensive review of all topics on the new hybrid-digital exam</li><li>Customize a study plan and target areas of improvement by using our diagnostic answer key</li><li>Practice drills at the end of each content review chapter, plus step-by-step walk-throughs of sample exam questions</li><li>Exclusive online digital flashcards to hone essential concepts</li></ul> Premium Practice for AP Excellence <ul><li>5 full-length practice tests (2 in the book, 3 online)</li><li>Detailed answer explanations to help you learn from mistakes</li><li>Online tests provided as both digital versions (with timer option to simulate exam experience), and as downloadable PDFs (with interactive elements mimicking the exam interface)</li><li>Get more via your online student tools—including a list of key terms and concepts, study plans, and exam updates</li></ul>

5 Practice Tests + Digital Practice Online + Content Review

Princeton Review AP Statistics Premium Prep, 22nd Edition

Intelligent Data-Driven Systems and Artificial Intelligence

This book aims to serve as a comprehensive resource that equips readers with the knowledge and practical skills needed to navigate the intricacies of fuzzy set theory, t-norms, and their integration into decision-aided intelligent systems. It provides a comprehensive understanding of aggregation operators and their role in data fusion, risk analysis, and expert opinion aggregation.<ul> <li>New aggregation operators, entropy measures, t-norm, and t-conorm structures are developed across multiple fuzzy set extensions to better model uncertainty and hesitation in decision-making.</li> <li>A wide range of real-world applications, including, tourism planning, smart cities, urban mobility, water security, smart campus automation, energy facility siting, and firefighting helicopter selection, are addressed using advanced multi-criteria decision making methods.</li> <li>The chapters collectively emphasize sustainable, data-driven, and uncertainty-aware decision support, contributing solutions in areas such as environmental protection, resource optimization, public services, and technological infrastructure.</li> <li>Innovative techniques, such as Lambert W-based aggregation operators, Choquet integral-based entropy, confidence-level aggregation, and fuzzy-machine learning hybrid models, improve the representation of interaction, ambiguity, and complexity in multi-criteria decision problems.</li> </ul>The text is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including mathematics, industrial engineering, supply chain management, operations research, manufacturing engineering, production engineering, and applied mathematics.

Aggregation in Decision-Aided Intelligent Systems

Fuzzy Sets and Triangular Norms

International Series on Actuarial Science

This comprehensive yet accessible guide to enterprise risk management for financial institutions contains all the tools needed to build and maintain an ERM framework. It discusses the internal and external contexts within which risk management must be carried out, and it covers a range of qualitative and quantitative techniques that can be used to identify, model and measure risks. This third edition has been thoroughly revised and updated to reflect new regulations and legislation. It includes additional detail on machine learning, a new section on vine copulas, and significantly expanded information on sustainability. A range of new case studies include Theranos and FTX. Suitable as a course book or for self-study, this book forms part of the core reading for the Institute and Faculty of Actuaries' examination in enterprise risk management.

Financial Enterprise Risk Management

Bayesian Workflow

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