We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood learning of Bayesian networks with belief propagation algorithms for approximate inference. Specifically we propose to combine the outer-loop step of convergent belief propagation algorithms with the M-step of the EM algorithm. This then yields an approximate EM algorithm that is essentially still d...

A new approach for signal parametrization, which consists of a specific regression model incorporating a discrete hidden logistic process, is proposed. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The parameters of the hidden logistic process, in the inner loop of the EM algorithm, are estimated using a mul...

In this overview report we present known results and open problems in connection with the minimal state space realization problem in the max-plus algebra, which is a framework that can be used to model a class of discrete event systems. 1 Description of the problem Given an n×n matrix A, an n×1 vector b and a 1×n vector c one can construct the sequence gi, i = 1, 2, . . ., where gi is defined b...

Scalable cluster analysis addresses the problem of processing large data sets with limited resources, e.g., memory and computation time. A data summarization or sampling procedure is an essential step of most scalable algorithms. It forms a compact representation of the data. Based on it, traditional clustering algorithms can process large data sets efficiently. However, there is little work on...

Discrete choice models are appealing for airline revenue management (RM) because they offer a means to profitably exploit preferences for attributes such as time of day, routing, brand and price. They are also good at modeling demand for unrestricted fare class structures, which are widespread throughout the industry. However, there is little empirical research on the practicality and effective...

The expectation maximization (EM) algorithm is popular in estimating the parameters of the statistical models. In this paper, we consider application of the EM algorithm to Maximum Likelihood estimation. A Hidden Markov Model (HMM) formulation is used and EM algorithm is applied to estimate the parameters of the HMM which, in turn, are used to estimate received amplitudes of the users. The prop...

Background: In this study, Quantitative 32P bremsstrahlung planar and SPECT imaging and consequent dose assessment were carried out as a comprehensive phantom study to define an appropriate method for accurate Dosimetry in clinical practice. Materials and Methods: CT, planar and SPECT bremsstrahlung images of Jaszczak phantom containing a known activity of 32P were acquired. In addition, Phanto...

The maximum likelihood (ML) technique plays an important role in direction-of-arrival (DOA) estimation. In this paper, we employ and design the expectation–conditional maximization either (ECME) algorithm, a generalization of expectation–maximization for solving ML direction finding problem stochastic sources, which may be correlated, unknown nonuniform noise. Unlike alternating maximization, E...

Recently, a new blind adaptive deconvolution algorithm was proposed based on a new closed-form approximated expression for the conditional expectation (the expectation of the source input given the equalized or deconvolutional output) where the output and input probability density function (pdf) of the deconvolutional process were approximated with the maximum entropy density approximation tech...

Recent architectural and technological advances have led to the feasibility of a new class of massively parallel processing systems based on a fine-grain, message-passing computational model. These machines provide a new alternative for the development of fast, cost-efficient Maximum Likelihood-Expectation Maximization (ML-EM) algorithmic formulations. As an important first step in determining ...