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Privault N: Financial Risk Analytics

Financial Risk Analytics

A Mathematical Introduction with R and Python

Chapman & Hall/CRC Texts in Statistical Science

From Alan Turing breaking the Enigma code to ChatGPT reshaping how we work and create, artificial intelligence has always been powered by a single unifying principle: learning from data under uncertainty. This book reveals the mathematical machinery behind modern AI, weaving together Bayesian probability, statistical learning, and deep neural networks into a coherent intellectual framework. Whether you seek to understand why large language models hallucinate, how recommendation systems predict your preferences, or what makes reinforcement learning agents master complex games, this book equips you with both the theoretical foundations and practical intuitions that define the modern AI playbook.Key Features:<ul> <li>Builds Bayesian reasoning from first principles through historical examples like submarine search and WWII code-breaking</li> <li>Bridges classical statistics and deep learning, connecting linear regression to transformers</li> <li>Covers the complete AI stack: probability, decision theory, Gaussian processes, neural networks, CNNs, NLP, and LLMs</li> <li>Emphasizes uncertainty quantification-building systems that know what they don't know</li> <li>Practical applications across finance, healthcare, operations, and autonomous systems</li> <li>Emerging topics: AI agents, RLHF, and retrieval-augmented generation</li> </ul>Written for graduate students, data scientists, and quantitatively-minded practitioners, this book assumes comfort with calculus and basic probability while building sophisticated intuitions progressively. Business analysts will appreciate the decision-theoretic framing; engineers will value the architectural insights; researchers will find rigorous foundations for further study. Whether used as a course textbook, professional reference, or intellectual companion for understanding the AI revolution transforming every industry, this book delivers the rare combination of mathematical depth and accessible exposition that makes complex ideas genuinely understandable.

Foundations of Data Science

Bayes, AI, and Deep Learning

The Basics

Finance

Strategic Staffing, Sixth Edition equips both current and future managers with the knowledge and skills to adopt a strategic and contemporary approach to talent identification, attraction, selection, deployment, and retention. Grounded in research, this text covers modern staffing concepts and practices in an engaging and reader-friendly format.

Strategic Staffing

Build operational resilience that helps your organization withstand disruption, meet regulatory expectations and adapt to emerging risks.Operational Resilience in Practice is a practical guide for operational resilience, operational risk, business continuity and compliance professionals responsible for turning resilience into an effective organizational capability.Written by former regulator and experienced resilience professional Chris Knox, this book provides practical guidance across the entire operational resilience lifecycle. Drawing on interviews with leading practitioners and real-world examples, it shows how to strengthen resilience while navigating regulatory requirements, including the EU Digital Operational Resilience Act (DORA) and an increasingly complex risk landscape.Learn how to: - Define critical services and map their underlying dependencies - Set impact tolerances and strengthen resilience testing - Manage outsourcing, cloud, SaaS and concentration risks - Embed operational resilience into technology and systems architecture &gt;Covering scenario and stress testing, incident response and continuous improvement, Operational Resilience in Practice helps you build a robust resilience program that protects critical services, strengthens decision-making and enables your organization to respond and adapt when disruption occurs.Themes include: operational resilience, operational risk management, DORA, business continuity, resilience testing, third-party risk

A Guide to Framework Design and Implementation

Operational Resilience in Practice

Mastering Business Analytics

Data Analytics for Strategic Decisions explores how businesses can harness the power of data, technology, and analytics to make strategic decisions that drive growth and innovation. It provides a comprehensive guide to integrating business strategy with data-driven methodologies, offering practical tools and insights for modern organizations.Readers will gain a deep understanding of how to transform data into actionable insights, improving decision-making across marketing, finance, operations, and strategy. The book combines theoretical foundations with real-world applications, including case studies, datasets, and practical exercises. It also addresses emerging topics like AI, predictive analytics, and data governance, ensuring readers are equipped with cutting-edge knowledge and skills.This book is designed for undergraduate and master's students in Business Administration, Business Analytics, and Digital Transformation, as well as professionals seeking to enhance their data-driven decision-making capabilities. It is ideal for courses in Business Analytics, Data Analytics for Business, Business Intelligence, and Digital Transformation.The book includes supplementary materials such as datasets for practical exercises, case studies, and discussion questions, making it suitable for both classroom and online learning environments.

Fundamentals of Business Technology and AI Integration

Data Analytics for Strategic Decisions

Agricultural Statistics

Agricultural Statistics is published each year to meet the diverse need for a reliable reference book on agricultural production. It covers an extensive list of issues related to agriculture including livestock, farm values, crop production, and foreign trade. The estimates for crops, livestock, and poultry made by the U.S. Department of Agriculture are prepared mainly to give timely current state and national totals and averages. They are based on data obtained by sample surveys of farmers and of people who do business with farmers. The survey data are supplemented by information from the Census of Agriculture taken every five years. Being estimates, they are subject to revision as more data become available from commercial or government sources. Unless otherwise indicated, the totals for the United States shown in the various tables on area, production, numbers, price, value, supplies, and disposition are based on official estimates. They exclude states for which no official estimates are compiled. Professionals in farming, ranching, agricultural consulting, and animal science will find this most valuable.

Agricultural Statistics 2025

This book is a collection of new artificial intelligence (AI) methods, including new regression, machine learning, deep learning, hybrid, and ensemble models, published in recent years. The algorithms and structures of these methods are clearly explained so that readers can use them in their studies and projects. In addition, by presenting several examples of models, the strengths and weaknesses of each model are explained to help readers select the best model for each kind of dataset. Therefore, readers can purposefully and usefully select the appropriate models among all options. As a result, the first advantage of this book in comparison to other books is its presentation of new AI models in detail, comparing their capabilities and features, and an overview of some criteria to select the appropriate model for various datasets. Moreover, this book provides the required information on sustainable development, which students need to know, as well as advanced information and details for using new AI approaches for sustainable development goals.To improve the criteria of sustainable development, managing the current and future situation is one the most important factors and needs the prediction approaches for the future; for example, environmental indicators like the management of air pollution, climate change, waste, and energy consumption. Sustainability also involves other variables, such as social, technical, and economic indicators, which new AI can efficiently manage. Furthermore, new AI can increase the number of models and involve more indicators in multi-criteria decision analysis. Therefore, the accuracy of this type of analyses to have a sustainable production and development can improve through an increase in the number of options and indicators, while the cost and time of analyses will be decreased using the AI methods. This book provides many new, stronger AI approaches for the various studies in sustainable development that will be of interest to researchers and students.

New Machine Learning Methods for the Sustainable Development Goals

Frontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications

This book introduces cutting-edge computational optimization techniques designed especially for AI enabled systems. These techniques inculcate advanced algorithms with real world applications to enhance efficiency and accuracy. Its focus on the hybrid and ensemble models makes it stand out, presenting novel and creative solutions for the complex problems. Through case studies and real-world applications the book not only highlights the theoretical aspects of the optimization techniques but also provides a link between theory and practice. With the help of clear explanations and practical examples the book empowers the readers to delve into the complex problems and providing optimized solutions leading to enhancing the performance of AI enabled systems.&nbsp;The book explores the advanced computational optimization techniques covering wide range of topics like nature-inspired algorithms, metaheuristics and hybrid optimization methods. This book caters the need of AI researchers, data scientists and machine learning engineers who aim to optimize their models and algorithms. It will be a valuable resource for the academicians and students studying AI and its related subfield. Professionals in the field of finance, healthcare, agriculture, education where AI applications are prevalent, will benefit from the practical insights provided. Overall, it caters to anyone interested in enhancing AI systems through sophisticated optimization strategies.

Computational Optimization Techniques for Artificial Intelligence Enabled Environments

International Series in Operations Research & Management Science

This book presents advanced mathematical programming models for contemporary supply chain risk optimization problems. It focuses on cutting‑edge issues such as supply chain viability under long‑term disruption risks, including reshoring decisions; optimization of digital supply chain cybersecurity and physical resilience in cyber‑physical supply chains; and supply chain fairness with respect to cost–service trade‑offs, profit distribution, and the allocation of cybersecurity investments. It also introduces computationally efficient decision‑making frameworks for emerging challenges in global supply chain management, including the reduction of cyber and physical risks and the enhancement of resilience and fairness. Each chapter ends with a table summarizing major managerial insights and the end-of-chapter problems to help the reader a self-check of material comprehension and to encourage for a further self-study.The book is intended for practitioners, researchers, and master’s and PhD‑level students in supply chain management, industrial engineering, operations research, applied mathematics, and related fields. Only an introductory background in operations research and supply chain management is assumed.

Viability, Cybersecurity, and Fairness

Optimization Models for Supply Chains

Patterns of Economic Change by State and Area

Patterns of Economic Change by State and Area: Income, Employment, and Gross Domestic Product is a special edition of Business Statistics of the United States. It presents data on personal income, employment, and gross domestic product for the United States as a whole, and by region, state, and metropolitan statistical area (MSA). Data on personal income and employment extends back to 1960 for the states and regions and to 1970 for the MSAs. Patterns of Economic Change complements other Bernan Press titles such as the State and Metropolitan Area Data Book and County and City Extra. In contrast to their predominantly current and detailed cross-section data on states and metropolitan areas, this book contributes historical time-series measurements of key aggregates that show how the economies of regions, states, and metropolitan areas have responded over time to cyclical currents and long-term trends.Statistics at the state level provide a framework for analyzing current economic conditions in each state and can serve as a basis for decision making.For example: ·Federal government agencies use the statistics as a basis for allocating funds and determining matching grants to states. The statistics are also used in forecasting models to project energy and water use. ·State governments use the statistics to project tax revenues and the need for public services. ·Academic regional economists use the statistics for applied research. ·Businesses, trade associations, and labor organizations use the statistics for market research.

Income, Employment, and Gross Domestic Product

Patterns of Economic Change by State and Area 2027

Business Statistics of the United States

Business Statistics of the United States is a comprehensive and practical collection of data from as early as 1913 that reflects the nation's economic performance. It provides several years of annual, quarterly, and monthly data in industrial and demographic detail, including key indicators such as: gross domestic product, personal income, spending, saving, employment, unemployment, the capital stock, and more. Business Statistics of the United States is the best place to find historical perspectives on the U.S. economy. Of equal importance to the data are the introductory highlights, extensive notes, and figures for each chapter that help users to understand the data, use them appropriately, and, if desired, seek additional information from the source agencies. Business Statistics of the United States provides a rich and deep picture of the American economy and contains approximately 3,500 time series. The data are predominantly from federal government sources, including: ·Board of Governors of the Federal Reserve System ·Bureau of Economic Analysis ·Bureau of Labor Statistics ·Census Bureau ·Employment and Training Administration ·Energy Information Administration ·Federal Housing Finance Agency ·Department of the Treasury

Patterns of Economic Change

Business Statistics of the United States 2026

Multi-Genre Creative Writing

A Writer's Craft

Financial Risk Analytics

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