Maximum Likelihood (ML) estimation is extensively used for estimating emission densities from clumped and incomplete nzeasurement data in Positron Emission Tomography (PEU modality. Reconstruction produced by ML-algorithm has been found noisy because it does not make use of available prior knowledge. Bayesian estimation provides such a platform for the inclusion of prior knowledge in the recons...

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...

An expectation-maximization (EM) algorithm for learning sparse and overcomplete representations is presented in this paper. We show that the estimation of the conditional moments of the posterior distribution can be accomplished by maximum a posteriori estimation. The approximate conditional moments enable the development of an EM algorithm for learning the overcomplete basis vectors and inferr...

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...

In this correspondence, we compare the expectation maximization (EM) algorithm with another iterative approach, namely, the iterative conditional estimation (ICE) algorithm, which was formally introduced in the field of statistical segmentation of images. We show that in case the probability density function (PDF) belongs to the exponential family, the EM algorithm is one particular case of the...

return maximization or risk minimization is goal in portfolio optimization based on mean variance theory. the structure of correlation matrices and individual variance of each asset are two main factors in optimization with risk minimization object. it’s necessary to use appropriate variance and correlation coefficient for time series with clustering volatilities feature, too. in this research,...

in this paper the conditional location problem is discussed. conditional location problems have a wide range of applications in location science. a new meta-heuristic algorithm for solving conditional p-median problems is proposed and results are compared to those of the previous studies. this algorithm produces much better results than the previous formulations.

We propose a method for estimating probability density functions and conditional density functions by training on data produced by such distributions. The algorithm employs new stochastic variables that amount to coding of the input, using a principle of entropy maximization. It is shown to be closely related to the maximum likelihood approach. The encoding step of the algorithm provides an est...

Asymptotic and exact conditional approaches have often been used for testing agreement between two raters with binary outcomes. The exact conditional approach is guaranteed to respect the test size as compared to the traditionally used asymptotic approach based on the standardized Cohen's kappa coefficient. An alternative to the conditional approach is an unconditional strategy which relaxes th...