Abbreviations: BN, Bayesian network; IBTS, Internatio International Council for the Exploration of the Sea; C pelagics; SP, small piscivorous; LP, large piscivorous a primary production; DAG, directed acyclic graph; distribution; CPT, conditional probability table; DBN, dyn hidden Markov model; ARHMM, autoregressive hidde variable; EM, Expectation Maximization algorithm; SSE, s ⁎ Corresponding ...

We introduce a new class of “maximization expectation” (ME) algorithms where we maximize over hidden variables but marginalize over random parameters. This reverses the roles of expectation and maximization in the classical EM algorithm. In the context of clustering, we argue that these hard assignments open the door to very fast implementations based on data-structures such as kdtrees and cong...

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 paper we propose a predictive approach for dynamic load balancing. This approach involves predicting unbalanced load distribution using Estimation Maximization (EM) algorithm, and migrating local jobs based on the estimation average. This strategy is an improvement to the existing approaches because by using EM algorithm, we only extract sample workload information which is assumed to f...

We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation–Maximization (EM) algorithm, a “winner take all” version of the EM algorithm reminiscent of the K-means algorithm, and model-based hierarchical agglomerative clustering. We learn naive-Bayes models with a hidden ro...

As an automatic method of determining model complexity using the training data alone, Bayesian linear regression provides us a principled way to select hyperparameters. But one often needs approximation inference if distribution assumption is beyond Gaussian distribution. In this paper, we propose a Bayesian linear regression model with Student-t assumptions (BLRS), which can be inferred exactl...

Clustering multivariate data that are contaminated by noise is a complex issue, particularly in the framework of mixture model estimation because noisy data can significantly affect the parameters estimates. This paper addresses this problem with respect to likelihood maximization using the Expectation-Maximization algorithm. Two different approaches are compared. The first one consists in defi...

Computerized tomography (CT) plays a critical role in modern medicine. However, the radiation associated with CT is significant. Methods that can enable CT imaging with less radiation exposure but without sacrificing image quality are therefore extremely important. This paper introduces a novel method for enabling image reconstruction at lower radiation exposure levels with convergence analysis...

Two major reasons for the popularity of the EM algorithm are that its maximum step involves only complete-data maximum likelihood estimation, which is often computationally simple, and that its convergence is stable, with each iteration increasing the likelihood. When the associated complete-data maximum likelihood estimation itself is complicated, EM is less attractive because the M-step is co...

We explore the idea of overrelaxation for accelerating the expectation-maximization (EM) algorithm, focusing on preserving its simplicity and monotonic convergence properties. It is shown that in many cases a trivial modification in the M-step results in an algorithm that maintains monotonic increase in the log-likelihood, but can have an appreciably faster convergence rate, especially when EM ...