Bayesian algorithms have lately been used in a large variety of applications. This paper proposes a new methodology for hyperparameter initialization in the Variational Bayes (VB) algorithm. We employ a dual expectationmaximization (EM) algorithm as the initialization stage in the VB-based learning. In the first stage, the EM algorithm is used on the given data set while the second EM algorithm...

INTRODUCTION Maximising the (log) likelihood (logL) in restricted maximum likelihood (REML) estimation of variance components almost invariably represents a constrained optimisation problem. Iterative algorithms available to solve this problem differ substantially in computational resources needed, ease of implementation, sensitivity to choice of starting values and rates of convergence. One of...

Many problems in scientific research and engineering applications can be decomposed into the constrained optimization problems. Most of them are the nonlinear programming problems which are very hard to be solved by the traditional methods. In this paper, an electromagnetism-like mechanism (EM) algorithm, which is a meta-heuristic algorithm, has been improved for these problems. Firstly, some m...

We take beneet from a causal Markov model deened on a quadtree to derive a multiresolution EM algorithm for unsupervised image classiication. This algorithm is an eecient alternative to expensive or approximate EM algorithms associated with Markov Random Fields. We show on synthetic and real images that our algorithm also provides good or even better results than those obtained by spatial MRF m...

0.1 Bound optimization; auxiliary functions . . . . . . . . . . . . . . . . . . . . . 2 0.2 The EM algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 0.3 EM may be used to optimize the log-posterior instead of the log-likelihood . . 5 0.4 Example: Mixture models and spike sorting . . . . . . . . . . . . . . . . . . . 5 0.5 Example: Spike sorting given stimulus observa...

A new approach to finding good local maxima of the likelihood function based on synthesizing information from two local maxima is presented. We investigate the coupled EM algorithm (CoEM) for coupling local maxima solutions from two separate EM runs for the multinomial mixture model. The CoEM algorithm probabilistically splits and merges multiple latent states based on conditional independence ...

In this paper, we address the problem of blind separation of convolutive mixtures of spatially and temporally independent sources modeled with mixtures of Gaussians. We present an EM algorithm to compute Maximum Likelihood estimates of both the separating filters and the source density parameters, whereas in the state-of-the-art separating filters are usually estimated with gradient descent tec...

Most of the researchers in the application areas usually use the EM algorithm to *nd estimators of the normal mixture distribution with unknown component speci*c variances without knowing much about the properties of the estimators. It is unclear for which situations the EM algorithm provides “good” estimators, good in the sense of statistical properties like consistency, bias, or mean square e...

We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics , possible world semantics with a probability distribution which is unconditionally a...

The EM algorithm is the standard tool for maximum likelihood estimation in )nite mixture models. The main drawbacks of the EM algorithm are its slow convergence and the dependence of the solution on both the stopping criterion and the initial values used. The problems referring to slow convergence and the choice of a stopping criterion have been dealt with in literature and the present paper de...