Title:

Contextual probability, Journal of Telecommunications and Information Technology, 2003, nr 3

Creator:

Wang, Hui

Subject and Keywords:

data mining ; machine learning ; mathematical foundations ; uncertainty ; knowledge representation

Description:

In this paper we present a new probability function G that generalizes the classical probability function. A mass function is an assignment of basic probability to some context (events, propositions). It represents the strength of support for some contexts in a domain. A context is a subset of the basic elements of interest in a domain – the frame of discernment. It is a medium to carry the “probabilistic” knowledge about a domain. The G function is defined in terms of a mass function under various contexts. G is shown to be a probability function satisfying the axioms of probability. Therefore G has all the properties attributed to a probability function. If the mass function is obtained from probability function by normalization, then G is shown to be a linear function of probability distribution and a linear function of probability. With this relationship we can estimate probability distribution from probabilistic knowledge carried in some contexts without any model assumption.

Publisher:

Instytut Łączności - Państwowy Instytut Badawczy, Warszawa

Date:

2003, nr 3

Resource Type:

artykuł

Format:

application/pdf

Resource Identifier:

ISSN 1509-4553, on-line: ISSN 1899-8852

DOI:

10.26636/jtit.2003.3.186

ISSN:

1509-4553

eISSN:

1899-8852

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności