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Abrevaya J: Probability and Statistics
for Economics and Business

Probability and Statistics for Economics and Business

An Introduction Using R

Modern Trends In Financial Engineering

This introductory/intermediate level textbook focuses on mathematical programming and its applications. It introduces basic linear programming -- the easiest form of mathematical programming with emphasis on economic interpretation of the model solution. Well-known applications of linear programming to problems in business and agriculture are presented.The text then extends into more advanced forms of mathematical programming including quadratic and integer programming. These models include the introduction of risk and uncertainty into decision-making and a class of models known as price endogenous models in which market equilibrium analyses can be modelled. Integer programming includes conditional decision-making model, machinery selection, and a class of models known as supply chain models.Supplements are provided to assist solution of the models using either GAMS or Excel, the two most widely used software packages for solution of mathematical programming models.

Applied Mathematical Programming For Business And Economics

This comprehensive compendium addresses a critical need in the AI and machine learning era by bridging foundational information theory (IT) concepts with practical applications in statistical learning. Unlike traditional IT textbooks, this volume emphasizes how IT principles, such as entropy and source coding, underpin modern machine learning techniques like cross-entropy, decision trees, and evidence-lower bounds.This unique book connects IT with probabilistic inference, illustrated through real-world applications, such as decoding LDPC codes for error correction. The inclusion of the Lea probabilistic programming package is particularly valuable for pedagogy, offering students a hands-on tool to solve numerical problems and reinforce theoretical concepts.The useful reference text benefits professionals, researchers, academics and students in the fields of calculus and probability, communications and information sciences.

Introduction To Information Theory And Probabilistic Inference, An

Pelican Books

'Probably the UK's greatest living statistician' Telegraph From the UK's 'statistical national treasure', a clever and data-driven guide to how we can live with risk and uncertainty We live in a world where uncertainty is inevitable. How should we deal with what we don't know? And what role do chance, luck and coincidence play in our lives? David Spiegelhalter has spent his career dissecting data in order to understand risks and assess the chances of what might happen in the future. In The Art of Uncertainty, he gives readers a window onto how we can all do this better. In engaging, crystal-clear prose, he takes us through the principles of probability, showing how it can help us think more analytically about everything from medical advice to pandemics and climate change forecasts, and explores how we can update our beliefs about the future in the face of constantly changing experience. Along the way, he explains why roughly 40% of football results come down to luck rather than talent, how the National Risk Register assesses near-term risks to the United Kingdom, and why we can be so confident that two properly shuffled packs of cards have never, ever been in the exact same order. Drawing on a wide range of captivating real-world examples, this is an essential guide to navigating uncertainty while also having the humility to admit what we do not know

How to Navigate Chance, Ignorance, Risk and Luck

The Art of Uncertainty

Hypocrisy++: On Philosophy Of Probability And Sociology Of Ideologies

A Statistical Guide to Certainty in an Uncertain World

What Are the Odds?

Chapman & Hall/CRC Biostatistics Series

Age-Period-Cohort Models

Integrated Management Systems: A Professional Handbook

Inside Rugby's Tactical and Data Revolution

Attacking the Space

Scaling (zooming in or out) is taking a close or distant view of an object. Infinite scaling (the scaling parameter tends to zero or infinity) may lead to a limit which is self-similar and much simpler than the original object. For instance, the classical Donsker's theorem says that all weakly dependent stationary processes with finite variance scale to Brownian motion at large scales. In the case of a random field indexed by two-dimensional parameter, both types of scaling can be anisotropic, meaning that the horizontal and vertical axes are scaled at different rate determined by the ratio γ&gt;0 of the scaling exponents along the axes. The natural questions under such scaling are whether the scaling limits exist for any γ&gt;0, and what are these limits are. The book tries to answer these questions. It introduces the concept of scaling transition and discusses its existence for a natural class of planar random fields including Gaussian, linear and some nonlinear ones. The scaling limits are identified and exhibit a surprising trichitomy at a critical point γ0, with 'unbalanced' limits having unusual path properties and dependence structure along one of the coordinate axes. Scaling transition occurs in applied sciences (telecommunications and econometrics) when aggregating independent processes with long-range dependence in which case 'unbalanced' limits are classical Gaussian or stable random fields.

Anisotropic Scaling Of Random Fields With Long-range Dependence: Scaling Limits Of Random Fields With Applications

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

This book presents an overview of the diverse literature on change-point methodology and describes some unifying themes and recent breakthroughs. It can be used as a second-year Ph.D. level course in statistics. It is expected to be useful to researchers and graduate students, not only in statistics but also in engineering, biomedicine, economics and finance.

Change-point Methodology and Applications

This book illustrates statistics with the R programming language, an emerging tool in data science. Filled with techniques for data science and analytics programming, the book is written for students and professionals. It is focused on learning outcomes and problem solving.

Demystifying Probability and Statistics for Data Scientists with R

Gibbs Measures In Machine Learning

Probability and Statistics for Economics and Business

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