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Adversarial Risk Analysis
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
Bayesian Analysis of Stochastic Process Models
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.
Robust Bayesian Analysis
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 wid...
Sensitivity Analysis in Multi-objective Decision Making
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 trea...
Algorithmic Decision Theory
This book constitutes the conference proceedings of the 7th International Conference on Algorithmic Decision Theory, ADT 2021, held in Toulouse, France, in November 2021. The 27 full papers presented were carefully selected from 58 submissions. The papers focus on algorithmic decision theory broadly defined, seeking to bring together researchers and practitioners coming from diverse areas of computer science, economics and operations research in order to improve the theory and practice of modern decision support.
Decision Theory and Decision Analysis: Trends and Challenges
Decision Theory and Decision Analysis: Trends and Challenges is divided into three parts. The first part, overviews, provides state-of-the-art surveys of various aspects of decision analysis and utility theory. The second part, theory and foundations, includes theoretical contributions on decision-making under uncertainty, partial beliefs and preferences. The third section, applications, reflects the real possibilities of recent theoretical developments such as non-expected utility theories, multicriteria decision techniques, and how these improve our understanding of other areas including artificial intelligence, economics, and environmental studies.
Decision Theory and Decision Analysis: Trends and Challenges
Decision Theory and Decision Analysis: Trends and Challenges is divided into three parts. The first part, overviews, provides state-of-the-art surveys of various aspects of decision analysis and utility theory. The second part, theory and foundations, includes theoretical contributions on decision-making under uncertainty, partial beliefs and preferences. The third section, applications, reflects the real possibilities of recent theoretical developments such as non-expected utility theories, multicriteria decision techniques, and how these improve our understanding of other areas including artificial intelligence, economics, and environmental studies.
Decision Behaviour, Analysis and Support
A multi-disciplinary exploration of how we can help decision makers to deliberate and make better decisions.
Expert Judgement in Risk and Decision Analysis
This book pulls together many perspectives on the theory, methods and practice of drawing judgments from panels of experts in assessing risks and making decisions in complex circumstances. The book is divided into four parts: Structured Expert Judgment (SEJ) current research fronts; the contributions of Roger Cooke and the Classical Model he developed; process, procedures and education; and applications. After an Introduction by the Editors, the first part presents chapters on expert elicitation of parameters of multinomial models; the advantages of using performance weighting by advancing the “random expert” hypothesis; expert elicitation for specific graphical models; modelling depende...
Perception as Bayesian Inference
Bayesian probability theory has emerged not only as a powerful tool for building computational theories of vision, but also as a general paradigm for studying human visual perception. This 1996 book provides an introduction to and critical analysis of the Bayesian paradigm. Leading researchers in computer vision and experimental vision science describe general theoretical frameworks for modelling vision, detailed applications to specific problems and implications for experimental studies of human perception. The book provides a dialogue between different perspectives both within chapters, which draw on insights from experimental and computational work, and between chapters, through commentaries written by the contributors on each others' work. Students and researchers in cognitive and visual science will find much to interest them in this thought-provoking collection.
