A note on how Renyi entropy can create a spectrum of probabilistic merging operators

Title:

A note on how Renyi entropy can create a spectrum of probabilistic merging operators

Creator:

Adamčík, Martin

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:f009534f-6096-48f3-ac88-7c3150faa0dd
uuid:f009534f-6096-48f3-ac88-7c3150faa0dd
doi:10.14736/kyb-2019-4-0605

Subject:

Kullback-Leibler divergence, probabilistic merging, information geometry, and Rényi entropy

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this paper we present a result that relates merging of closed convex sets of discrete probability functions respectively by the squared Euclidean distance and the Kullback-Leibler divergence, using an inspiration from the Rényi entropy. While selecting the probability function with the highest Shannon entropy appears to be a convincingly justified way of representing a closed convex set of probability functions, the discussion on how to represent several closed convex sets of probability functions is still ongoing. The presented result provides a perspective on this discussion. Furthermore, for those who prefer the standard minimisation based on the squared Euclidean distance, it provides a connection to a probabilistic merging operator based on the Kullback-Leibler divergence, which is closely connected to the Shannon entropy.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

605-617

Source:

Kybernetika | 2019 Volume:55 | Number:4

Harvested from:

CDK

Metadata only:

false