Relative Entropy, Probabilistic Inference and AI

TL;DR

This paper reviews the fundamental properties of relative entropy and explores its significance in probabilistic inference and AI applications, highlighting its theoretical importance and potential uses.

Contribution

It provides a comprehensive review of relative entropy's properties and discusses its role and potential applications in AI and probabilistic inference.

Findings

Relative entropy has key properties useful for inference.

It plays a significant role in probabilistic systems.

Potential applications in AI are identified.

Abstract

Various properties of relative entropy have led to its widespread use in information theory. These properties suggest that relative entropy has a role to play in systems that attempt to perform inference in terms of probability distributions. In this paper, I will review some basic properties of relative entropy as well as its role in probabilistic inference. I will also mention briefly a few existing and potential applications of relative entropy to so-called artificial intelligence (AI).