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Bibliographic Details
Main Author: Suliman, Sammy
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2406.15398
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author Suliman, Sammy
author_facet Suliman, Sammy
contents The purpose of this thesis is to convey the basic concepts of information geometry and its applications to non-specialists and those in applied fields, assuming only a first-year undergraduate background in calculus, linear algebra, and probability theory / statistics. We first begin with an introduction to the EM algorithm, providing a typical use case in Python, before moving to an overview of basic Riemannian geometry. We then introduce the core concepts of information geometry and the $em$ algorithm, with an explicit calculation of both the $e$ and $m$ projection, before closing with a discussion of an important application of this research to the field of deep learning, providing a novel implementation in Python.
format Preprint
id arxiv_https___arxiv_org_abs_2406_15398
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle The EM Algorithm in Information Geometry
Suliman, Sammy
History and Overview
Information Theory
The purpose of this thesis is to convey the basic concepts of information geometry and its applications to non-specialists and those in applied fields, assuming only a first-year undergraduate background in calculus, linear algebra, and probability theory / statistics. We first begin with an introduction to the EM algorithm, providing a typical use case in Python, before moving to an overview of basic Riemannian geometry. We then introduce the core concepts of information geometry and the $em$ algorithm, with an explicit calculation of both the $e$ and $m$ projection, before closing with a discussion of an important application of this research to the field of deep learning, providing a novel implementation in Python.
title The EM Algorithm in Information Geometry
topic History and Overview
Information Theory
url https://arxiv.org/abs/2406.15398