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Statistical Decision Theory and Bayesian Analysis

as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes;

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Bayesian Decision Analysis

Bayesian decision analysis supports principled decision making in complex domains. This textbook takes the reader from a formal analysis;

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Bayesian Analysis of Stochastic Process Models

, computational, inference, forecasting, decision making and important applied models. Key features: * Explores Bayesian analysis of models based on;

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Bayesian Cost-Effectiveness Analysis of Medical Treatments

Cost-effectiveness analysis is becoming an increasingly important tool for decision making in the health systems. Cost-Effectiveness;

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Bayesian Cost-Effectiveness Analysis of Medical Treatments

Cost-effectiveness analysis is becoming an increasingly important tool for decision making in the health systems. Cost-Effectiveness;

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Robust Bayesian Analysis

aspects and describes decision-theoretical axiomatisa- tions leading to the robust Bayesian paradigm, motivating reasons for which robust analysis;

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Rational Decision and Causality

, economists, statisticians and psychologists concerned with decision making and the employment of Bayesian principles. His analysis of the;

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Rational Decision and Causality

, economists, statisticians and psychologists concerned with decision making and the employment of Bayesian principles. His analysis of the;

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An Introduction to Bayesian Inference and Decision

An Introduction to Bayesian Inference and Decision is een boek van Robert L Winkler;

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Statistical Computing in Nuclear Imaging

Bayesian statistics and concepts and is highly focused on the computational aspects of Bayesian data analysis of photon-limited data acquired;

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Statistical Computing in Nuclear Imaging

Bayesian statistics and concepts and is highly focused on the computational aspects of Bayesian data analysis of photon-limited data acquired;

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Risk Assessment and Decision Analysis with Bayesian Networks

practical real-world problem solving and model building, as opposed to algorithms and theory, Risk Assessment and Decision Analysis with Bayesian;

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Modeling in Medical Decision Making

. Simulation-based Bayesian methods are especially promising, as they provide a unified framework for data collection, inference, and decision making;

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Rational Decisions

It is widely held that Bayesian decision theory is the final word on how a rational person should make decisions. However, Leonard Savage;

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Rethinking the Foundations of Statistics

This important collection of essays is a synthesis of foundational studies in Bayesian decision theory and statistics. An overarching topic;

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Cambridge Studies in Probability, Induction and Decision Theory

This important collection of essays is a synthesis of foundational studies in Bayesian decision theory and statistics. An overarching topic;

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Market Research and Modeling: Progress and Prospects

in marketing and marketing research. This work emphasizes new developments in Bayesian Decision Analysis, Multivariate Analysis, Multidimensional;

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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;

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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;

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Bayesian Model Selection and Statistical Modeling

basis for the analysis of these criteria. In addition, the author discusses how Bayesian model averaging can simultaneously treat both model and;

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Bayesian Networks

, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips;

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Maximum Entropy and Bayesian Methods

Mexico, USA, from July 31 to August 4, 1995. Maximum entropy and Bayesian methods are widely applied to statistical data analysis and scientific;

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Bayesian Networks and Decision Graphs

This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical;

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Biostatistics

Logistic regression and adjusted odds ratios Survival analysis Hierarchical models and meta-analysis Decision theory and sample size determination;

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Current Trends in Bayesian Methodology with Applications

latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis;

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Current Trends in Bayesian Methodology with Applications

latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis;

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Introduction to Statistical Decision Theory

Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision;

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