This paper considers the design of close−to−optimal receivers in the presence of a timing uncertainty. The problem is placed into the factor−graph and the sum−product (SP) algorithm framework. A simplified version of the SP algorithm is considered and the expectation−maximization (EM) algorithm is used to implement it. The proposed approach, combining the SP and EM algorithms, is shown to outpe...

CONTENTS 1. Introduction 2. Image and blur models 3. Maximum likelihood (ML) parameter identification 3.1. Formulation 3.2. Constraints on the unknown parameters 4. ML parameter identification via the expectation-maximization (EM) algorithm 4.1. The EM algorithm in the linear Gaussian case 4.2. Choices of complete data 4.2.1. {x,y} as the complete data 4.2.2. {x,v} as the complete data Abstract...

The application of Kalman filtering methods and maximum likelihood parameter estimation to models of commodity prices and futures prices has been considered by several authors. The usual method of finding the maximum likelihood parameter estimates (MLEs) is to numerically maximize the likelihood function. We present, as an alternative to numerical maximization of the likelihood, a filter-based ...

Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, re...

The expectation-maximization (EM) algorithm aims to nd the maximum of a log-likelihood function, by alternating between conditional expectation (E) step and maximization (M) step. This survey rst introduces the general structure of the EM algorithm and the convergence guarantee. Then Gaussian Mixture Model (GMM) are employed to demonstrate how EM algorithm could be applied under Maximum-Likelih...

Information geometry of partial likelihood is constructed and is used to derive the em-algorithm for learning parameters of a conditional distribution model through information -theoretic projections. To construct the coordinates of the information geometry, an Expectation-Maximization (EM) framework is described for the distribution learning problem using the Gaussian mixture probability model...

Expectation maximization (EM) is a popular algorithm for parameter estimation in situations with incomplete data. The EM algorithm has, despite its popularity, the disadvantage of often converging to local but non-global optima. Several techniques have been proposed to address this problem, for example initializing EM from multiple random starting points and then selecting the run with the high...

This introduction to the expectation–maximization (EM) algorithm provides an intuitive and mathematically rigorous understanding of EM. Two of the most popular applications of EM are described in detail: estimating Gaussian mixture models (GMMs), and estimating hidden Markov models (HMMs). EM solutions are also derived for learning an optimal mixture of fixed models, for estimating the paramete...

Mixture probability densities are popular models that are used in several data mining and machine learning applications, e.g., clustering. A standard algorithm for learning such models from data is the Expectation-Maximization (EM) algorithm. However, EM can be slow with large datasets, and therefore approximation techniques are needed. In this paper we propose a variational approximation to th...