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Displaying similar documents to “Comment on 'On some statistical paradoxes and non-conglomerability' by Bruce Hill.”

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).

Alternative definitions of conditional possibilistic measures

Ivan Kramosil (1998)

Kybernetika

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The aim of this paper is to survey and discuss, very briefly, some ways how to introduce, within the framework of possibilistic measures, a notion analogous to that of conditional probability measure in probability theory. The adjective “analogous” in the last sentence is to mean that the conditional possibilistic measures should play the role of a mathematical tool to actualize one’s degrees of beliefs expressed by an a priori possibilistic measure, having obtained some further information...

Pre-supports of linear probability measures and linear Lusin measurable functionals

W. Słowikowski

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CONTENTS1. Introduction, review of the results, examples...................................................................................52. Linear probability measures and their representations................................................................103. Linear Lusin measurable functionals...............................................................................................164. Pre-supports and a modification of the definition of the linear probability measure................235....

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.