David Rios Insua's Books

Adversarial Risk Analysis

By: David L. Banks,Jesus M. Rios Aliaga,David Rios Insua

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Adversarial Risk Analysis

By: David L. Banks,Jesus M. Rios Aliaga,David Rios Insua

Winner of the 2017 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)A relatively new area of research, adversarial risk analysis (ARA) informs decision making when there are intelligent opponents and uncertain outcomes. Adversarial Risk Analysis develops methods for allocating defensive or offensive resources against

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Page Count

220

Publisher

CRC Press

Published Date

ISBN 10

1498712401

ISBN 13

9781498712408

Bayesian Analysis of Stochastic Process Models

By: David Insua,Fabrizio Ruggeri,Mike Wiper

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Bayesian Analysis of Stochastic Process Models

By: David Insua,Fabrizio Ruggeri,Mike Wiper

Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.

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Page Count

315

Publisher

John Wiley & Sons

Published Date

ISBN 10

0470744537

ISBN 13

9780470744536

Sensitivity Analysis in Multi-objective Decision Making

By: David Rios Insua

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Sensitivity Analysis in Multi-objective Decision Making

By: David Rios Insua

The axiomatic foundations of the Bayesian approach to decision making assurne precision in the decision maker's judgements. In practicc, dccision makers often provide only partial and/or doubtful information. We unify and expand results to deal with those cases introducing a general framework for sensitivity analysis in multi-objective decision making. We study first decision making problems under partial information. We provide axioms leading to modelling preferences by families of value functions, in problems under certainty, and moJelling beliefs by families of probability distributions and preferences by familics of utility functions, in problems under uncertainty. Both problems are treated in parallel with the same parametric model. Alternatives are ordered in a Pareto sense, the solution of the problem being the set of non dominated alternatives. Potentially optimal solutions also seem acceptable, from an intuitive point of view and due to their relation with the nondominated ones. Algorithms are provided to compute these solutions in general problems and in cases typical in practice: linear and bilinear problems. Other solution concepts are criticised on the grounds of being ad hoc. In summary, we have a more ro bust theory of decision making based on a weaker set ofaxioms, but embodying coherence, since it essentially implies carrying out a family of coherent dccision anitlyses.

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Page Count

204

Publisher

Springer Science & Business Media

Published Date

ISBN 10

3642516564

ISBN 13

9783642516566

Bayesian Methods for Nonlinear Classification and Regression

By: David G. T. Denison,Christopher C. Holmes,Bani K. Mallick,Adrian F. M. Smith

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Bayesian Methods for Nonlinear Classification and Regression

By: David G. T. Denison,Christopher C. Holmes,Bani K. Mallick,Adrian F. M. Smith

Bei der Regressionsanalyse von Datenmaterial erhält man leider selten lineare oder andere einfache Zusammenhänge (parametrische Modelle). Dieses Buch hilft Ihnen, auch komplexere, nichtparametrische Modelle zu verstehen und zu beherrschen. Stärken und Schwächen jedes einzelnen Modells werden durch die Anwendung auf Standarddatensätze demonstriert. Verbreitete nichtparametrische Modelle werden mit Hilfe von Bayes-Verfahren in einen kohärenten wahrscheinlichkeitstheoretischen Zusammenhang gebracht.

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Page Count

302

Publisher

John Wiley & Sons

Published Date

ISBN 10

0471490369

ISBN 13

9780471490364

Public Administration

Understanding Management, Politics, and Law in the Public Sector

By: David H. Rosenbloom,Robert S. Kravchuk,Richard M Clerkin

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Public Administration

Understanding Management, Politics, and Law in the Public Sector

By: David H. Rosenbloom,Robert S. Kravchuk,Richard M Clerkin

The ninth edition of Public Administration: Understanding Management, Politics, and Law in the Public Sector grounds students in the fundamentals of public administration while embracing its complexity. It describes, explains, and analyzes public administration through the lenses of three well-established perspectives: management, politics, and law. In addition to thoroughly refreshed examples and case studies, significant updates to this new edition include the following: The discussion of management has been collapsed into a single category, contemporary public management, to better reflect the blending of traditional/orthodox and new public management approaches in the field. Significant changes to federal administration initiated by the Trump administration, the emergence of "hyper-" partisanship, major court decisions affecting public administration, and newer scholarship and foci in public administration. A thoroughly rewritten chapter on budgeting and public finance. New public governance material is incorporated throughout the book, including collaborative models for coordinating administration with private organizations, particularly nonprofits. Additional attention is paid to public participation in public administration, including public administration's potential contribution to strengthening democratic citizenship. Thorough discussion of the latest managerial techniques and concepts as well as the contemporary performance orientation in the public sector. Downloadable instructor support materials including Key Points, Discussion and/or Test Questions, Multiple Choice Questions, True or False Questions, and an Answer Key to accompany each chapter in the book. Together these revisions reinvigorate the book yet retain its core structure, ideas, and familiarity for students and instructors alike. While the new edition retains its focus on the U.S. context, the focus on managerial, legislative, and judicial functions lends itself well to public administration in many developed nations, making the book a popular choice with instructors around the globe. This time-tested and fully up-to-date textbook is required reading for all students of public administration, public management, and nonprofit management.

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Page Count

645

Publisher

Routledge

Published Date

ISBN 10

1000426262

ISBN 13

9781000426267

An Introduction to Universal Artificial Intelligence

By: Marcus Hutter,David Quarel,Elliot Catt

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An Introduction to Universal Artificial Intelligence

By: Marcus Hutter,David Quarel,Elliot Catt

An Introduction to Universal Artificial Intelligence provides the formal underpinning of what it means for an agent to act intelligently in an unknown environment. First presented in Universal Algorithmic Intelligence (Hutter, 2000), UAI offers a framework in which virtually all AI problems can be formulated, and a theory of how to solve them. UAI unifies ideas from sequential decision theory, Bayesian inference, and algorithmic information theory to construct AIXI, an optimal reinforcement learning agent that learns to act optimally in unknown environments. AIXI is the theoretical gold standard for intelligent behavior. The book covers both the theoretical and practical aspects of UAI. Bayesian updating can be done efficiently with context tree weighting, and planning can be approximated by sampling with Monte Carlo tree search. It provides algorithms for the reader to implement, and experimental results to compare against. These algorithms are used to approximate AIXI. The book ends with a philosophical discussion of Artificial General Intelligence: Can super-intelligent agents even be constructed? Is it inevitable that they will be constructed, and what are the potential consequences? This text is suitable for late undergraduate students. It provides an extensive chapter to fill in the required mathematics, probability, information, and computability theory background.

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Page Count

517

Publisher

CRC Press

Published Date

ISBN 10

1003821979

ISBN 13

9781003821977

Adversarial Risk Analysis

By: David L. Banks,Jesus M. Rios Aliaga,David Rios Insua

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Adversarial Risk Analysis

By: David L. Banks,Jesus M. Rios Aliaga,David Rios Insua

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220

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CRC Press

Published Date

ISBN 10

1498712401

ISBN 13

9781498712408

Sensitivity Analysis in Multi-objective Decision Making

By: David Rios Insua

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Sensitivity Analysis in Multi-objective Decision Making

By: David Rios Insua

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Page Count

204

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Springer Science & Business Media

Published Date

ISBN 10

3642516564

ISBN 13

9783642516566

Robust Bayesian Analysis

By: David Rios Insua,Fabrizio Ruggeri

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Robust Bayesian Analysis

By: David Rios Insua,Fabrizio Ruggeri

Robust Bayesian analysis aims at overcoming the traditional objection to Bayesian analysis of its dependence on subjective inputs, mainly the prior and the loss. Its purpose is the determination of the impact of the inputs to a Bayesian analysis (the prior, the loss and the model) on its output when the inputs range in certain classes. If the impact is considerable, there is sensitivity and we should attempt to further refine the information the incumbent classes available, perhaps through additional constraints on and/ or obtaining additional data; if the impact is not important, robustness holds and no further analysis and refinement would be required. Robust Bayesian analysis has been widely accepted by Bayesian statisticians; for a while it was even a main research topic in the field. However, to a great extent, their impact is yet to be seen in applied settings. This volume, therefore, presents an overview of the current state of robust Bayesian methods and their applications and identifies topics of further in terest in the area. The papers in the volume are divided into nine parts covering the main aspects of the field. The first one provides an overview of Bayesian robustness at a non-technical level. The paper in Part II con cerns foundational aspects and describes decision-theoretical axiomatisa tions leading to the robust Bayesian paradigm, motivating reasons for which robust analysis is practically unavoidable within Bayesian analysis.

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Page Count

448

Publisher

Springer

Published Date

ISBN 10

0387988661

ISBN 13

9780387988665

Book's by David Rios Insua

Adversarial Risk Analysis

Adversarial Risk Analysis

Bayesian Analysis of Stochastic Process Models

Bayesian Analysis of Stochastic Process Models

Sensitivity Analysis in Multi-objective Decision Making

Sensitivity Analysis in Multi-objective Decision Making

Bayesian Methods for Nonlinear Classification and Regression

Bayesian Methods for Nonlinear Classification and Regression

Public Administration

Public Administration

An Introduction to Universal Artificial Intelligence

An Introduction to Universal Artificial Intelligence

Adversarial Risk Analysis

Adversarial Risk Analysis

Sensitivity Analysis in Multi-objective Decision Making

Sensitivity Analysis in Multi-objective Decision Making

Robust Bayesian Analysis

Robust Bayesian Analysis

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