Displaying similar documents to “Sufficiency in bayesian models”

Foundations of subjective probability and decision making: Discussion.

Irving John Good, Ludovico Piccinato, Cesáreo Villegas, James M. Dickey, Morris H. DeGroot, Donald A. S. Fraser, Simon French, Dennis V. Lindley (1980)

Trabajos de Estadística e Investigación Operativa

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Discussion on the papers by Girón, F. J. and Ríos, S., Quasi-Bayesian behaviour: a more realistic approach to dicision making? and by Hill, B. M., On finite additivity, non-conglomerability and statistical paradoxes, both of them part of a round table on Foundations of Subjective Probability and Decision Making held in the First International Congress on Bayesian Methods (Valencia, Spain, 28 May - 2 June 1979).

Some history of the hierarchical Bayesian methodology.

Irving John Good (1980)

Trabajos de Estadística e Investigación Operativa

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A standard tecnique in subjective Bayesian methodology is for a subject (you) to make judgements of the probabilities that a physical probability lies in various intervals. In the Bayesian hierarchical technique you make probability judgements (of a higher type, order, level or stage) concerning the judgements of lower type. The paper will outline some of the history of this hierarchical technique with emphasis on the contributions by I. J. Good because I have read every word written...

On the central limit theorem on IFS-events.

Jozefina Petrovicová, Riecan Beloslav (2005)

Mathware and Soft Computing

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A probability theory on IFS-events has been constructed in [3], and axiomatically characterized in [4]. Here using a general system of axioms it is shown that any probability on IFS-events can be decomposed onto two probabilities on a Lukasiewicz tribe, hence some known results from [5], [6] can be used also for IFS-sets. As an application of the approach a variant of Central limit theorem is presented.

Bayes robustness via the Kolmogorov metric

Agata Boratyńska, Ryszard Zieliński (1993)

Applicationes Mathematicae

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An upper bound for the Kolmogorov distance between the posterior distributions in terms of that between the prior distributions is given. For some likelihood functions the inequality is sharp. Applications to assessing Bayes robustness are presented.

Robust inference in probability under vague information.

Giuliana Regoli (1996)

Mathware and Soft Computing

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Vague information can be represented as comparison of previsions or comparison of probabilities, and a robust analysis can be done, in order to make inference about some quantity of interest and to measure the imprecision of the answers. In particular, in some decision problems the answer can be unique.