We have been developing a general symbolic-statistical modeling language [6, 19, 20] based on the logic programming framework that semantically uni es (and extends) major symbolic-statistical frameworks such as hidden Markov models (HMMs) [18], probabilistic contextfree grammars (PCFGs) [23] and Bayesian networks [16]. The language, PRISM, is intended to model complex symbolic phenomena governe...

SUMMARY In this article we investigate the relationship between the two popular algorithms, the EM algorithm and the Gibbs sampler. We show that the approximate rate of convergence of the Gibbs sampler by Gaussian approximation is equal to that of the corresponding EM type algorithm. This helps in implementing either of the algorithms as improvement strategies for one algorithm can be directly ...

A new modification of Electromagnetism-like (EM) algorithm which incorporating the Record-to-Record Travel (RRT) local search algorithm; namely MEMR has been developed to solve the problem of Inverse Kinematics (IK) for a four Degree-of-Freedom (DOF) manipulator. The proposed method is able to generate multiple robot configurations for the IK test performed at different end effect or positions....

An optimal maximal ratio combiner (MRC) based on the expectation-maximization (EM) algorithm is developed for noisy constant envelope signals transmitted over a Rayleigh fading channel. Instead of using a transmitted pilot signal with the data to estimate the combiner gains, the EM algorithm is used to perform this estimation. In the developed MRC, estimation of the transmitted data sequence is...

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...

In this paper, a new design algorithm for estimating the parameters of Gaussian Mixture Models is presented. The method is based on the matching pursuit algorithm. Speaker Identification is considered as an application area. The estimated GMM performs as good as the EM algorithm based model. Computational complexity of the proposed method is much lower than the EM algorithm.

In medium-resolution (7-10 A) cryo-electron microscopy (cryo-EM) density maps, alpha helices can be identified as density rods whereas beta-strand or loop regions are not as easily discerned. We are proposing a computational protein structure prediction algorithm "EM-Fold" that resolves the density rod connectivity ambiguity by placing predicted alpha helices into the density rods and adding mi...

External memory (EM) algorithms are designed for computational problems in which the size of the internal memory of the computer is only a small fraction of the problem size. For certain large scale applications this is necessarily true. Typically, the cost models proposed for external memory algorithms have measured only the number of I/O operations, and the algorithms have been specially craf...

This laboratory explores the use of the expectation-maximization (EM) algorithm for the estimate of parameters. In particular, we will use the EM algorithm for two applications: clustering, and hidden Markov model training. You are encouraged to implement your solutions to this laboratory in Matlab. For an original derivation of the monotone convergence of the likelihood for the EM algorithm, t...

In data mining applications, there are various kinds of missing values in experimental datasets. Non-substitution or inappropriate treatment of missing values has a high probability to cause a lot of warnings or errors. Besides, many classification algorithms are very sensitive to the missing values. Because of these, handling the missing values is an important phase in many classification or d...