2012Unpublished venueRequires access

A metrology-sound probability-possibility transformation for joint distributions

Alessandro Ferrero, Marco Prioli, Simona Salicone

Open publisher page 2 citations

Abstract

In the recent years the possibility theory has been investigated by many Authors in the field of mathematics and engineering. A possibility distribution is, from the mathematical point of view, a generalization of a probability distribution, since it can represent a family of probability distributions. Given a probability distribution, different probability-possibility transformations have been defined, which transform the probability distribution into different possibility distributions. Probability-possibility transformations are useful in any problem where statistical data must be dealt within the possibility theory, together with other heterogeneous uncertain and imprecise data. This paper generalizes these transformations to two-dimensional distributions with a particular care to the maximum specificity principle, so that joint probability distributions can be suitably transformed into maximally specific joint possibility distributions.

About this research paper

What this paper is about

In the recent years the possibility theory has been investigated by many Authors in the field of mathematics and engineering. A possibility distribution is, from the mathematical point of view, a generalization of a probability distribution, since it can represent a family of probability distributions. Given a probability distribution, different probability-possibility transformations have been defined, which transform the probability distribution into different possibility distributions. Probability-possibility transformations are useful in any problem where statistical data must be dealt within the possibility theory, together with other heterogeneous uncertain and imprecise data. This paper generalizes these transformations to two-dimensional distributions with a particular care to the maximum specificity principle, so that joint probability distributions can be suitably transformed into maximally specific joint possibility distributions.

Why it matters

OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

In the recent years the possibility theory has been investigated by many Authors in the field of mathematics and engineering. A possibility distribution is, from the mathematical point of view, a generalization of a probability distribution, since it can represent a family of probability distributions. Given a probability distribution, different probability-possibility transformations have been defined, which transform the probability distribution into different possibility distributions. Probability-possibility transformations are useful in any problem where statistical data must be dealt within the possibility theory, together with other heterogeneous uncertain and imprecise data. This paper generalizes these transformations to two-dimensional distributions with a particular care to the maximum specificity principle, so that joint probability distributions can be suitably transformed into maximally specific joint possibility distributions.

Key concepts: Joint probability distribution, Probability distribution, Convolution of probability distributions, K-distribution, Applied probability, Probability theory, Transformation (genetics), Statistical physics

Related papers

Back to paper searchBrowse research topicsOriginal source
A metrology-sound probability-possibility transformation for joint distributions — Research Paper | ScholarLens