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Catalogue complet

Gelman Andrew: Bayesian Workflow

Bayesian Workflow

A novel framework for how entrepreneurs can use Bayesian reasoning to make decisions. This edited volume introduces and explores the concept of Bayesian entrepreneurship, a novel framework for understanding entrepreneurial decision-making under uncertainty. It brings together contributions from leading scholars to examine how entrepreneurs form beliefs about opportunities, learn through experimentation, and make strategic decisions. The Bayesian approach emphasizes the critical interaction between an entrepreneur’s prior beliefs and their capacity for learning and experimentation to update those beliefs. This dynamic process allows entrepreneurs to formulate and test theories about value creation and capture, attract resources and capabilities, and implement effective entrepreneurial strategies. The volume is organized around four key themes: <ul><li>the foundations and distinguishing features of Bayesian entrepreneurship </li><li>how entrepreneurs form and update prior beliefs in entrepreneurial decision-making</li><li>the role of experimentation and learning in entrepreneurial strategy</li><li>applications to entrepreneurship education and practice</li></ul> Bayesian Entrepreneurship offers both theoretical contributions advancing our understanding of entrepreneurial decision-making and practical insights for entrepreneurs and educators.

A novel framework for how entrepreneurs can use Bayesian reasoning to make decisions. This edited volume introduces and explores the concept of Bayesian entrepreneurship, a novel framework for understanding entrepreneurial decision-making under uncertainty. It brings together contributions from leading scholars to examine how entrepreneurs form beliefs about opportunities, learn through experimentation, and make strategic decisions. The Bayesian approach emphasizes the critical interaction between an entrepreneur’s prior beliefs and their capacity for learning and experimentation to update those beliefs. This dynamic process allows entrepreneurs to formulate and test theories about value creation and capture, attract resources and capabilities, and implement effective entrepreneurial strategies. The volume is organized around four key themes: <ul><li>the foundations and distinguishing features of Bayesian entrepreneurship </li><li>how entrepreneurs form and update prior beliefs in entrepreneurial decision-making</li><li>the role of experimentation and learning in entrepreneurial strategy</li><li>applications to entrepreneurship education and practice</li></ul> Bayesian Entrepreneurship offers both theoretical contributions advancing our understanding of entrepreneurial decision-making and practical insights for entrepreneurs and educators.

Bayesian Entrepreneurship

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

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

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

State Profiles

State Profiles 2025: The Population and Economy of Each U.S. State has been completely updated and provides a wealth of current, authoritative, and comprehensive data on key demographic and economic indicators for each U.S. state and the District of Columbia. Each state is covered by a compact standardized chapter that allows for easy comparisons and timely analysis between the states. A ten-page profile for each U.S. state plus the District of Columbia provides reliable, up-to-date information on a wide range of topics, including: population, labor force, income and poverty, government finances, crime, education, health insurance coverage, voting, marital status, migration, and more. If you want a single source of key demographic and economic data on each of the U.S. states, there is no other book like this. It provides an overview of the U.S. economy which creates a framework for understanding the state information. State Profiles is primarily useful for public, school, and college and university libraries, as well as for economic and sociology departments. However, anyone needing state-level information including students, state officials, investors, economic analysts, and concerned citizens will find its wealth of data and analysis absolutely essential.

The Population and Economy of Each U.S. State

State Profiles 2026

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

What drives your company's value?

Are you a business owner, wondering what its true worth is? If you wanted to sell, what is the ultimate price you could get? Do you know how to achieve that price?

This isn't just another business book; it's your definitive compass to mastering EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization)-the vital metric investors use to assess a company's true worth.

In The 12 Levers of Business Performance , seasoned expert Andreas Gfesser, drawing from over 35 years of experience transforming businesses into powerhouses, reveals the strategies crucial for growth and profitability. Through a practical, step-by-step approach, guided by the relatable journey of fictional CEO Bob, you will discover:

The 12 Value Levers that will systematically enhance your business operations and financial performance, preparing your company for its highest possible valuation.
The Closed-Loop Value Enhancement Process, a proven framework for continuous improvement that aligns every aspect of your business for lasting success.
How to build your own EBITDA Integration Strategy, a comprehensive guide that captures your company's historical trajectory and outlines its future strategy for maximizing returns.



Whether you want to sell, attract investors, or simply optimize your company's financial health, this book provides insights and actionable tools to elevate your business. Learn to think like an investor, leverage strategic planning, and achieve the sustained profitability you deserve.

It's time to unlock your business's full potential. 

Your journey to maximizing EBITDA value begins here.

Maximize Profit, Manage Cash Flow, and Enhance Your Value

The 12 Levers of Business Performance

A step-by-step guide for anyone challenged by the many subtleties of sampling particulate materials. The only comprehensive document merging the famous works of P. Gy, I. Visman, and C.O. Ingamells into a single theory in a logical way - the most advanced book on sampling that can be used by all sampling practitioners around the world.

Theory of Sampling and Sampling Practice

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

Probability and Statistics for Engineering and the Sciences, Loose-Leaf Version

Integrating Statistical and Management Methods of Quality, Fourth Edition

A First Course in Quality Engineering

Dig deep to discover the truth about numbers and statistics in the media Wrong Number: How to Extract Truth From a Blizzard of Quantitative Disinformation shows readers how to diagnose statistical errors in journal articles and media accounts of prominent public issues. In addition to the mistakes or tricks with numbers other people use to lose the truth, this book also offers more positive material, showing how to use numbers to find truth. Written by Aaron Brown, prominent media figure, former professor, sports bettor, and poker player, and host of the popular ReasonTV series Wrong Number, this book answers questions including: <ul> <li>Did California's minimum wage hike really create more jobs?</li> <li>Do studies show that weed causes strokes and heart attacks?</li> <li>Are poor geniuses being shut out of science?</li> <li>What is the real health risk of vaping?</li> <li>Does gun control work?</li> </ul> Wrong Number: How to Extract Truth From a Blizzard of Quantitative Disinformation is an entertaining and eye-opening read for students in quantitative programs of study, along with all individuals curious as to whether or not they're being told the truth from their favorite media organizations.

How to Extract Truth From a Blizzard of Quantitative Disinformation

Wrong Number

Bayesian Workflow

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