In this paper we investigate an efficient implementation of the Monte Carlo EM algorithm based on Quasi-Monte Carlo sampling. The Monte Carlo EM algorithm is a stochastic version of the deterministic EM (Expectation-Maximization) algorithm in which an intractable E-step is replaced by a Monte Carlo approximation. Quasi-Monte Carlo methods produce deterministic sequences of points that can signi...

The authors define ordered subset processing for standard algorithms (such as expectation maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass through all the subsets, in each subset using the current estimate to initialize applicatio...

The paper introduces Voting EM, an adaptive online learning algorithm of Bayesian network parameters. Voting EM is an extension of the EM( ) algorithm suggested by [1]. We show convergence properties of the Voting EM that uses a constant learning rate. We use the convergence properties to formulate an error driven scheme for adapting the learning rate. The resultant algorithm converges with the...

In this paper, we present a new parallel EM algorithm with the optimal data replication on a hypercube niultiprocessor. Although data replication has been frequently used to reduce data sharing overhead, none of the parallel EM algorithms have attempted to optimize the data replication. To maximize efficiency of the proposed parallel EM algorithm, data replication have been optimized through st...

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...

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...

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...

The EM algorithm is a popular and useful algorithm for "nding the maximum likelihood estimator in incomplete data problems. Each iteration of the algorithm consists of two simple steps: an E-step, in which a conditional expectation is calculated, and an M-step, where the expectation is maximized. In some problems, however, the EM algorithm cannot be applied since the conditional expectation req...

Based on the electromagnetism-like algorithm, an evolutionary algorithm, improved EM algorithm with genetic algorithm technique (IEMGA), for optimization of fractional-order PID (FOPID) controller is proposed in this article. IEMGA is a population-based meta-heuristic algorithm originated from the electromagnetism theory. It does not require gradient calculations and can automatically converge ...

4 The Expectation-Maximization algorithm 7 4.1 Jointly-non-concave incomplete log-likelihood . . . . . . . . . . . 7 4.2 (Possibly) Concave complete data log-likelihood . . . . . . . . . . 8 4.3 The general EM derivation . . . . . . . . . . . . . . . . . . . . . 9 4.4 The E& M-steps . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.5 The EM algorithm . . . . . . . . . . . . . . . . . . ....