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

Smoothing Spline ANOVA Models (Springer Series in Statistics. Hinkley Theoretical Statistics. It is one of the first comprehensive models that combine statistical decision theory in form of Bayesian analysis with a real options framework for projects exposed to different sources of uncertainty. Indeed, behaviour in the known and knowable space builds on scientific knowledge, the archetypal example of combined explicit knowledge, that is, scientific models and theories. And analysis that are collected together. (Springer Series in Statistics) [Hardcover. The value of the loss function itself is a random quantity because it depends on the outcome of a random variable X. The regulation of externally paced human locomotion in virtual reality. Chapman and Hall/CRC 1974 (£44.99 paperback). 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. Statistical decision theory and Bayesian analysis. Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. Berger Statistical Decision Theory and Bayesian Analysis. Series: Springer Texts in Statistics. I explore its value, particularly in its .. Springer 1985 (£52.99 hardback). 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. David Snowden's Cynefin framework, introduced to articulate discussions of sense-making, knowledge management and organisational learning, has much to offer discussion of statistical inference and decision analysis. Neuroscience Letters, 275, 171-174.