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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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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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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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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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Statistical Computing in Nuclear Imaging

in tomographic measurements. Basic statistical concepts, elements of decision theory, and counting statistics, including models of photon-limited;

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Statistical Computing in Nuclear Imaging

in tomographic measurements. Basic statistical concepts, elements of decision theory, and counting statistics, including models of photon-limited;

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Bayesian Decision Analysis

contains basic material on subjective probability theory and multi-attribute utility theory, event and decision trees, Bayesian networks, influence;

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Bayesian Cost-Effectiveness Analysis of Medical Treatments

problem, and gives an overview of the frequentist and Bayesian statistical approaches for decision making. Basic notions on decision theory such;

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Bayesian Cost-Effectiveness Analysis of Medical Treatments

problem, and gives an overview of the frequentist and Bayesian statistical approaches for decision making. Basic notions on decision theory such;

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Bayesian Theory

, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian;

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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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Modeling in Medical Decision Making

amounts of data. In parallel, advances in computing have led to a host of new and powerful statistical tools to support decision making;

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Bayesian Approaches in Oncology Using R and OpenBUGS

foundation for later chapters. Many books on the bayesian approach and the statistical analysis are advanced, and many are theoretical. While most;

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Bayesian Theory and Methods with Applications

, environmental sciences, business and economics and social sciences, among others. This book explores the use of Bayesian analysis in the statistical;

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Statistical Paradigms

disciplines. For advances in theory, the topics include: Bayesian Inference, Directional Data Analysis, Distribution Theory, Econometrics and Multiple;

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Bayesian Model Selection and Statistical Modeling

Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model;

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Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists

statistical analysis and inference, remain relatively unknown among researchers and practitioners in this field. Through its inclusive, hands-on;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Statistical Inference

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian;

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Statistical Inference

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian;

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The Theory and Practice of Econometrics

. It examines the classical, the decision theory, and the Bayesian approaches, and contains material on single equation and simultaneous;

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Mathematical Foundations of Infinite-Dimensional Statistical Models

of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric;

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Likelihood and Bayesian Inference

choice and prediction from frequentist and Bayesian perspectives. This revised edition of the book Applied Statistical Inference has been;

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Likelihood & Bayesian Inference

choice and prediction from frequentist and Bayesian perspectives. This revised edition of the book Applied Statistical Inference has been;

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Data Analysis In Forensic Science

and presentation of data analyses are all examined from a Bayesian decision-theoretic perspective. A wide range of statistical methods;

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Case Studies in Bayesian Statistical Modelling and Analysis

in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: * Illustrates how to do Bayesian analysis;

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