Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy

TL;DR

This paper introduces a new approach for learning parameters in possibilistic networks from imprecise, multi-valued data, utilizing a novel sampling process and a likelihood function based on the connection between random sets and possibility theory.

Contribution

It proposes a sampling process and a likelihood function for parameter learning in possibilistic networks from imprecise datasets, advancing the handling of uncertain data.

Findings

Developed a possibilistic networks sampling process

Introduced a likelihood function linking random sets and possibility theory

Enabled parameter learning from multi-valued, imprecise data

Abstract

There has been an ever-increasing interest in multidisciplinary research on representing and reasoning with imperfect data. Possibilistic networks present one of the powerful frameworks of interest for representing uncertain and imprecise information. This paper covers the problem of their parameters learning from imprecise datasets, i.e., containing multi-valued data. We propose in the rst part of this paper a possibilistic networks sampling process. In the second part, we propose a likelihood function which explores the link between random sets theory and possibility theory. This function is then deployed to parametrize possibilistic networks.