The Expectation Maximization Algorithm: A short tutorial
نویسنده
چکیده
This tutorial discusses the Expectation Maximization (EM) algorithm of Dempster, Laird and Rubin [1]. The approach taken follows that of an unpublished note by Stuart Russel, but fleshes out some of the gory details. In order to ensure that the presentation is reasonably self-contained, some of the results on which the derivation of the algorithm is based are presented prior to the main results. The EM algorithm has become a popular tool in statistical estimation problems involving incomplete data, or in problems which can be posed in a similar form, such as mixture estimation [3, 4]. The EM algorithm has also been used in various motion estimation frameworks [5] and variants of it have been used in multiframe superresolution restoration methods which combine motion estimation along the lines of [2].
منابع مشابه
Latent Dirichlet Allocation: Towards a Deeper Understanding
The aim of this tutorial is to introduce the reader to Latent Dirichlet Allocation (LDA) for topic modeling. This tutorial is not all-inclusive and should be accompanied/crossreferenced with Blei et al. (2003). The unique aspect of this tutorial is that I provide a full pseudo-code implementation of variational expectation-maximization LDA and an R code implementation at http://highenergy.physi...
متن کاملA Tutorial on the Expectation-Maximization Algorithm Including Maximum-Likelihood Estimation and EM Training of Probabilistic Context-Free Grammars
The paper gives a brief review of the expectation-maximization algorithm (Dempster, Laird, and Rubin 1977) in the comprehensible framework of discrete mathematics. In Section 2, two prominent estimation methods, the relative-frequency estimation and the maximum-likelihood estimation are presented. Section 3 is dedicated to the expectation-maximization algorithm and a simpler variant, the genera...
متن کاملExpectation Maximization: a Gentle Introduction
This tutorial was basically written for students/researchers who want to get into first touch with the Expectation Maximization (EM) Algorithm. The main motivation for writing this tutorial was the fact that I did not find any text that fitted my needs. I started with the great book “Artificial Intelligence: A modern Approach”of Russel and Norvig [6], which provides lots of intuition, but I was...
متن کاملEM Demystified: An Expectation-Maximization Tutorial
After a couple of disastrous experiments trying to teach EM, we carefully wrote this tutorial to give you an intuitive and mathematically rigorous understanding of EM and why it works. We explain the standard applications of EM to learning Gaussian mixture models (GMMs) and hidden Markov models (HMMs), and prepare you to apply EM to new problems. This tutorial assumes you have an advanced under...
متن کامل