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Statistical decision theory and bayesian analysis

Statistical decision theory and bayesian analysis. James O. Berger

Statistical decision theory and bayesian analysis


Statistical.decision.theory.and.bayesian.analysis.pdf
ISBN: 0387960988,9780387960982 | 316 pages | 8 Mb


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Statistical decision theory and bayesian analysis James O. Berger
Publisher: Springer




Throughout his career, Kadane, the Leonard J. Van der Linde, “Bayesian Measures of Model Complexity and Fit,” J. PITTSBURGH—"Principles of Uncertainty," written by Carnegie Mellon University's Joseph B. Numerical Analysis for Statisticians. A special very important problem of the statistical machine learning is the classification problem which can be regarded as a task of classifying some objects into classes in accordance with their properties or features. While an innocuous theory, practical use of the Bayesian approach requires consideration of complex practical issues, including the source of the prior distribution, the choice of a likelihood function, computation and summary of the posterior . For inference, a full report of the posterior distribution is the correct and final conclusion of a statistical analysis. Berger, Statistical Decision Theory and Bayesian Analysis, second ed. Statistical Decision Theory and Bayesian Analysis. (Jay) Kadane, has won the International Society for Bayesian Analysis' coveted DeGroot Prize. One of the directions for developing the corresponding methods is the fuzzy classification which applies the main ideas of fuzzy set theory to various classification problems. However, this may be impractical, particularly when the posterior is high-dimensional.

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