Displaying similar documents to “A probabilistic Proof of Bochner's Theorem on positive definite Functions”

An extended problem to Bertrand's paradox

Mostafa K. Ardakani, Shaun S. Wulff (2014)

Discussiones Mathematicae Probability and Statistics

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Bertrand's paradox is a longstanding problem within the classical interpretation of probability theory. The solutions 1/2, 1/3, and 1/4 were proposed using three different approaches to model the problem. In this article, an extended problem, of which Bertrand's paradox is a special case, is proposed and solved. For the special case, it is shown that the corresponding solution is 1/3. Moreover, the reasons of inconsistency are discussed and a proper modeling approach is determined by...

Large losses-probability minimizing approach

Michał Baran (2004)

Applicationes Mathematicae

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The probability minimizing problem for large losses of portfolio in discrete and continuous time models is studied. This gives a generalization of quantile hedging presented in [3].

Totally coherent set-valued probability assessments

Angelo Gilio, Salvatore Ingrassia (1998)

Kybernetika

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We introduce the concept of total coherence of a set-valued probability assessment on a family of conditional events. In particular we give sufficient and necessary conditions of total coherence in the case of interval-valued probability assessments. Some relevant cases in which the set-valued probability assessment is represented by the unitary hypercube are also considered.

Null events and stochastical independence

Giulianella Colleti, Romano Scozzafava (1998)

Kybernetika

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In this paper we point out the lack of the classical definitions of stochastical independence (particularly with respect to events of 0 and 1 probability) and then we propose a definition that agrees with all the classical ones when the probabilities of the relevant events are both different from 0 and 1, but that is able to focus the actual stochastical independence also in these extreme cases. Therefore this definition avoids inconsistencies such as the possibility that an event A ...

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.