In this paper the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation of a random vector is applied to the problem of symbol detection for CPM signals transmitted over timeselective Ftayleigh fading channels. This results in a soft-in soft-out (SISO) detection algorithm suitable for iterative detection/decoding schemes. Simulation results show that the error perfor...

This paper considers the problem of estimating the parameters of a bilinear system from input-output measurements. A novel approach to this problem is proposed, one based upon the so-called Expectation Maximisation algorithm, wherein maximum likelihood estimates are generated iteratively without the need for a gradient-based search algorithm. This simple method is shown to perform well in simul...

Abstract—An expectation-maximization (EM) algorithm for independent component analysis in the presence of gaussian noise is presented. The estimation of the conditional moments of the source posterior can be accomplished by maximum a posteriori estimation. The approximate conditional moments enable the development of an EM algorithm for inferring the most probable sources and learning the param...

We propose a new method for the Maximum Likelihood Estimator (MLE) of nonlinear mixed effects models when the variance matrix of Gaussian random effects has a prescribed pattern of zeros (PPZ). The method consists of coupling the recently developed Iterative Conditional Fitting (ICF) algorithm with the Expectation Maximization (EM) algorithm. It provides positive definite estimates for any samp...

The Expectation Maximisation (EM) algorithm is a popular technique for maximum likelihood in incomplete data models. In order to overcome its documented limitations, several stochastic variants are proposed in the literature. However, none of these algorithms is guaranteed to provide a global maximizer of the likelihood function. In this paper we introduce the MEM algorithm — a Metropolis versi...


 This paper discuss about the use face patteren recognition which is now days become popular especialy on smartphone lock screen system. The method used in this research are Expectation – Maximization (EM) Algorithm. EM Algorithm an iterative optimization for estimation of Maximum Likelihood (ML) incomplete data problems. there 2 stages, namely stage E (E-step) and M (M-step). These two s...

Practical applications of the theory of knowledge structures often rely on a probabilistic version, known as the basic local independence model. The paper outlines various procedures for estimating its parameters, including maximum likelihood (ML) via the expectation-maximization (EM) algorithm, the computationally efficient minimum discrepancy (MD) estimation as well as MDML, a hybrid method c...

In this letter, an iterative receiver with soft-decision feedback is derived by using the expectation–maximization algorithm for maximum a posteriori estimate of fast Rayleigh flat fading channels. Simulation results indicate that in a fast fading environment, the derived receiver can perform better than an iterative receiver with hard-decision feedback.