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Catalogue complet

Gelman Andrew: Bayesian Workflow

Bayesian Workflow

Chapman and Hall/CRC Series on Statistics in Business and Economics

Introduction to Bayesian Econometrics: A GUIded Toolkit Using R offers a practical, conceptually clear, and computationally accessible pathway into Bayesian data analysis. Designed for readers who wish to apply Bayesian methods without necessarily investing years in programming, the book combines rigorous treatment of foundational ideas with a graphical user interface (GUI) that allows users to run Bayesian regression models in a user-friendly environment.The first part develops the mathematical foundations of Bayesian inference by presenting all derivations step-by-step. This transparent treatment of conjugate models, including posterior analysis, marginal likelihoods, and posterior predictive distributions, provides readers with a strong theoretical base for the more advanced material that follows.The second part focuses on implementation. It introduces the custom GUI for readers with little or no programming experience, demonstrates how to fit Bayesian models using established R packages, and guides more advanced users through programming key components of Bayesian samplers from scratch. This integrated approach enables readers with different backgrounds to engage with Bayesian methods at their preferred level of computational depth.The third part extends the framework to modern Bayesian econometrics. It covers Bayesian machine learning, causal inference, and approximate methods, illustrating how Bayesian ideas can be applied to contemporary empirical challenges. By combining theory, software, and hands-on computation, the book provides a comprehensive entry point into both classical and modern Bayesian analysis.Across all parts, the book is designed to support a wide range of users -beginners, intermediate programmers, and advanced learners-. To the best of the author's knowledge, no existing text combines mathematical transparency, software accessibility, and modern Bayesian topics in a single, integrated resource.

A GUIded Toolkit using R

Introduction to Bayesian Econometrics

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

In today's data-driven business world, organizations struggle to extract actionable insights from vast amounts of information. This textbook bridges theory and practice, offering structured approaches to leverage data for enhanced decision-making through machine learning, AI, and optimization modeling-transforming raw data into competitive business intelligence.Decision Making and Analytics: Data-Driven Business Intelligence and Optimization features advanced analytical methodologies paired with real-world implementation studies, creating a progressive learning path from customer behavior analysis through hybrid decision models. By integrating fuzzy logic, neural networks, text mining, and reinforcement learning, it provides a comprehensive toolkit applicable across business scenarios, turning abstract concepts into practical solutions.Serving multiple audiences, this resource bridges the gap between classroom theory and industry practice for students in industrial engineering, business analytics, and management information systems. Practitioners gain applicable methodologies to improve organizational decision-making. At the same time, researchers benefit from cutting-edge approaches across various domains, and policymakers can develop data-informed strategies-addressing the growing demand for professionals who translate data into strategic decisions.The textbook employs open-ended questions that develop analytical reasoning skills without predetermined solutions, better preparing students for real-world complexity. Supporting materials include high-quality figure slides, as well as PowerPoint slides, for qualified adopters, enabling engaging learning environments that effectively communicate complex concepts while building practical, data-driven decision-making skills.

Data-Driven Business Intelligence and Optimization

Decision-Making and Analytics

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

Derivatives: Mathematical Foundations for Finance Students is written for students of finance and actuarial science who want to understand derivatives not just as formulas but as ideas that make sense. The goal is to build the mathematical foundations up step by step - starting from the familiar territory of first-year undergraduate probability and statistics and moving all the way to stochastic calculus and the principles of risk-neutral pricing, which form the heart of modern financial mathematics.This text will enable the reader to:¿ understand models of asset price behaviour and apply stochastic calculus ¿ use stochastic simulations in R to investigate processes and price options ¿ price options with the binomial model ¿ derive solutions to the Black-Scholes equation using risk-neutral pricing ¿ price interest-rate derivatives using Black's model, short-rate models and the Heath-Jarrow-Morton (HJM) framework ¿ tackle quantitative finance exam questions.It incorporates a range of learning features to aid student understanding, including boxed examples, end-of-chapter summaries, selected questions from Society of Actuaries (US) Quantitative Finance examinations and further reading suggestions. The book is also supported by a suite of digital learning resources, including PowerPoint slides, multiple choice questions, instructor manual/advice document for lecturers, and a test bank.Derivatives: Mathematical Foundations for Finance Students will appeal to both undergraduate and postgraduate students who wish to understand the principles of stochastic calculus and option pricing.

Mathematical Foundations for Finance Students

Derivatives

Empirical Finance: Theory and Application offers a modern, data-driven introduction to the field of finance, tailored for undergraduate students and practitioners seeking to bridge theory with real-world evidence. In an era defined by abundant data and computational power, this book emphasizes hands-on learning by integrating financial theory, empirical analysis, and practical implementation using Python and R. Each chapter balances intuitive explanations with mathematical rigor, ensuring that readers not only understand key concepts but also learn how to test them with actual data.Structured in two parts, the book begins with a thorough review of essential quantitative tools-optimization, probability, and statistics-providing the foundation needed for empirical work. The second part applies these tools to core topics in finance, including asset pricing, portfolio choice, market efficiency, event studies, and volatility modeling. Real-world examples and case studies-such as testing the Efficient Markets Hypothesis, analyzing stock splits, and evaluating the equity premium-bring the material to life and illustrate how empirical methods can validate or challenge economic intuition.A distinctive feature of this text is its emphasis on reproducibility and application. Code snippets, exercises, and datasets enable readers to replicate results and develop their own analyses. Topics like time-series properties of returns, portfolio management and behavioral finance are treated with both theoretical and empirical depth, preparing students for quantitative internships, graduate studies, or roles in the financial industry.Ideal for courses in Empirical Finance, Financial Econometrics, or Quantitative Finance, this book stands out for its clear exposition, relevance to contemporary practice, and commitment to evidence-based reasoning. It empowers a new generation of finance students to think critically, work with data, and understand markets not as a set of abstract rules, but as a dynamic interplay of economics, data, and technology.Key Features:· Seamlessly integrates hands-on coding in both Python and R with financial theory, enabling readers to replicate results and conduct their own empirical analysis.· Strikes a unique balance between financial intuition, mathematical clarity, and real-world application, avoiding the common extremes of abstract theory or mere data manipulation.· Structured in two distinct parts-first building essential quantitative tools (optimization, probability, statistics) before applying them to core finance topics-ensuring a solid foundation for empirical work.· Uses contemporary, relevant examples throughout, such as testing market anomalies, analyzing cryptocurrency returns, and conducting event studies on recent scandals.· Emphasizes a data-centric approach to validate or challenge economic reasoning, teaching students to treat finance as a dynamic, evidence-based discipline.

Theory and Application

Empirical Finance

Today, more than ever, Department Chairs need to be equipped with strategies to lead their departments to success in research. The Strategic Department Chair: Leading for Academic Research Success provides techniques not only to cultivate a department that is successful in research, but also to sustain that level of productivity. The book offers Department Chairs a valuable resource for negotiating the real-life challenges that they will face as academic leaders.Key Features:<ul> <li>Provides Ten Top Tips for Chairing for Research Success </li> <li>Provides step-by-step guidelines for mentoring new faculty to advance their research trajectories alongside broader strategies for developing their tenure and promotion portfolios</li> <li>Explains how to avoid common pitfalls, supplying critical tips for challenges common to Department Chairs, including a chapter on Facing Challenges: Assistant Professors without Success in Research</li> <li>Provides strategies to mentor mid-career faculty and win-win strategies for enhancing graduate student success while advancing faculty research success</li> <li>Illustrates key concepts with case studies and example initiatives that can be put into administrative practice</li> </ul>Written by a Department Chair at a Carnegie R1-level (highly research active) University and a former Associate Dean for Research with a history of continual funding from NIH and national foundations, The Strategic Department Chair: Leading for Academic Research Success provides a toolbox of approaches that can immediately be put into action. Therefore, this text is not only relevant for new Department Chairs, but also for existing Chairs, in a wide range of research-based areas including STEM (Science, Technology, Engineering, and Mathematics), Public Health, Medicine, and the Natural Sciences.

Leading for Academic Research Success

The Strategic Department Chair

International exclusive edition, with sprayed edges. 'A K-Pop version of Stephen King' - The Times One idol. Four fans. Worship's never been bloodier. Yosep is a K-pop idol with millions of adoring fans. But for four of them, a poster on the wall just won't cut it. They have a plan-a perfect, foolproof plan-to get their idol all to themselves. Kidnapping Yosep seemed like the ultimate act of love. But inside a secluded mansion, plagued by paranoia and with their grip on reality slowly loosening, the women use increasingly disturbing strategies to keep Yosep in their possession. As their angel's halo slips and their perfect plan unravels, the women must fight not only to keep him, but to keep their secret buried - at all costs. More than a little unhinged, Holy Boy is an exhilarating descent into the dark side of devotion. Translated from the Korean by Joheun Lee.

Holy Boy

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

The book addresses the underlying question of mis-specification in models estimated with spatial data. When spatial econometrics focusses on model estimation techniques, it is most often assumed that mis-specification is restricted to spatial autocorrelation and occasionally heteroskedasticity.

Introduction to Applied Spatial Econometrics

Unlock the full potential of quantitative trading with Prescriptions for Quant Traders Using R-a practical, hands-on guide for turning data into trading decisions.This book is written for quantitative traders, financial analysts, and data scientists who want more than theory. Organized into ten structured parts, it delivers step-by-step "prescriptions" using R-script-driven tasks that solve real-world trading problems. Each prescription is designed to be immediately actionable, so readers can move from concept to implementation without guesswork.What makes this book different is its clarity, utility, and its link to online video walk-throughs of R scripts. Modeled in the spirit of a Scientific American article, the text flows seamlessly without interruptions, yet a comprehensive bibliography ensures depth and rigor. Every chapter blends explanation with executable code, enabling traders to:<ul> <li> Design systematic trading strategies in R - and understand why they work.</li> <li> Apply robust statistical and econometric methods to financial data for better predictions.</li> <li> Evaluate risk and performance metrics to refine strategies with confidence.</li> <li> Automate workflows so insights move faster from model to market.</li> </ul>Whether you are a retail trader aiming to compete with professionals, a financial analyst seeking sharper models, or a data scientist expanding into trading, this book bridges the gap between theory and practice.Readers will find special value in the prescription-based approach. Just as a doctor prescribes remedies, these scripts deliver targeted solutions to trading challenges-from portfolio optimization and volatility forecasting to Bayesian inference and machine learning integration.In a marketplace where retail participation continues to grow, Prescriptions for Quant Traders Using R equips you with the knowledge, tools, and confidence to approach trading systematically. The result: deeper insights, stronger discipline, and strategies that stand the test of real-world markets.Whether you're coding your first model or refining a sophisticated strategy, this book provides the prescriptions you need to succeed.

Videos & Scripts

Prescriptions for Quant Traders Using R

Bayesian Workflow

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