Foundations of Info-Metrics

Modeling, Inference, and Imperfect Information

Inbunden, Engelska, 2018

AvAmos Golan

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Info-metrics is the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is at the intersection of information theory, statistical inference, and decision-making under uncertainty. It plays an important role in helping make informed decisions even when there is inadequate or incomplete information because it provides a framework to process available information with minimal reliance on assumptions that cannot be validated.In this pioneering book, Amos Golan, a leader in info-metrics, focuses on unifying information processing, modeling and inference within a single constrained optimization framework. Foundations of Info-Metrics provides an overview of modeling and inference, rather than a problem specific model, and progresses from the simple premise that information is often insufficient to provide a unique answer for decisions we wish to make. Each decision, or solution, is derived from the available input information along with a choice of inferential procedure. The book contains numerous multidisciplinary applications and case studies, which demonstrate the simplicity and generality of the framework in real world settings. Examples include initial diagnosis at an emergency room, optimal dose decisions, election forecasting, network and information aggregation, weather pattern analyses, portfolio allocation, strategy inference for interacting entities, incorporation of prior information, option pricing, and modeling an interacting social system. Graphical representations illustrate how results can be visualized while exercises and problem sets facilitate extensions. This book is this designed to be accessible for researchers, graduate students, and practitioners across the disciplines.

Produktinformation

Utgivningsdatum2018-01-18
Mått236 x 165 x 33 mm
Vikt930 g
FormatInbunden
SpråkEngelska
Antal sidor488
FörlagOUP USA
ISBN9780199349524

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Amos Golan is a professor of economics and directs the Info-Metrics Institute at American University. He is also an External Professor at the Santa Fe Institute and a Senior Associate at Pembroke College, Oxford. His research is primarily in the interdisciplinary field of info-metrics - the science and practice of information processing, modeling, inference, and problem solving with insufficient information. He has published in economics, econometrics, statistics, mathematics, physics and philosophy journals. His books include Maximum Entropy Econometrics: Robust Estimation with Limited Data (coauthored with Judge and Miller) and Information and Entropy Econometrics - A Review and Synthesis.

DedicationAcknowledgementsChapter 1 - Introduction Chapter 2 - Rational Inference: A Constrained Optimization FrameworkChapter 3 - The Metrics of Info-MetricsChapter 4 - Entropy MaximizationChapter 5 - Inference in The Real WorldChapter 6 - Advanced Inference in The Real WorldChapter 7: Efficiency, Sufficiency, and OptimalityChapter 8 - Prior InformationChapter 9 - A Complete Info-Metrics FrameworkChapter 10 - Modeling and TheoriesChapter 11 - Causal Inference via Constraint SatisfactionChapter 12 - Info-Metrics and Statistical Inference: Discrete ProblemsChapter 13 - Info-Metrics and Statistical Inference: Continuous ProblemsChapter 14 - New Applications Across DisciplinesEpilogueAppendicesList of SymbolsReferencesIndex

This book is a good mixture of theory and practical applications...The topics of the applications are well chosen; they represent contemporary tendencies in statistical science including a broad range of related subject material and interesting topics, from classical to modern. The tools and techniques that are present here can be applied in various disciplines like finance, banking, economics, accounting, marketing, social and health sciences and even to areas like psychology, engineering, and medicine...As such, there are more than good reasons why it should be strongly recommended to potential readers and users.

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