Markov or a maximum entropy random field model for a prior distribution may be viewed as a minimizer of a variational problem. Using notions from robust statistics, a variational filter referred to as a Huber gradient descent flow is proposed. It is a result of optimizing a Huber functional subject to some noise constraints and takes a hybrid form of a total variation diffusion for large gradie...

This contribution concerns a generalization of the Boltzmann Machine that allows us to use the learning rule for a much wider class of maximum likelihood and maximum a posteriori problems, including both supervised and unsupervised learning. Furthermore, the approach allows us to discuss regularization and generalization in the context of Boltzmann Machines. We provide an illustrative example c...

The feasibility of developing a neural network to perform nonlinear Bayesian estimation from sparse data is explored using an example from clinical pharmacology. The problem involves estimating parameters of a dynamic model describing the pharmacokinetics of the bronchodilator theophylline from limited plasma concentration measurements of the drug obtained in a patient. The estimation performan...

In order to improve the fusion quality of IKONOS multispectral (MS) and panchromatic (Pan) images, this paper proposes a fusion method using maximum likelihood (ML) estimation. The proposed method firstly uses the sensor characteristics to model the observation process of both MS and Pan images. Then, the cost function with respect to the estimated high-resolution MS images is constructed based...

The problem of joint blind channel estimation and multiple access interference (MAI) suppression for an asynchronous code-division multiple-access (CDMA) system is studied. A low-complexity sliding-window scheme based on the expectation maximization (EM) algorithm is developed for joint blind maximum a posteriori probability (MAP) multi-user detection (MUD) and stochastic maximum likelihood (ML...

Model-based clustering for functional data is considered. An alternative to model-based clustering using the functional principal components is proposed by approximating the density of functional random variables. The EM algorithm is used for parameter estimation and the maximum a posteriori rule provides the clusters. Simulation study and real data application illustrate the interest of the pr...

Statistical speech recognition using continuousdensity hidden Markov models (CDHMMs) has yielded many practical applications. However, in general, mismatches between the training data and input data significantly degrade recognition accuracy. Various acoustic model adaptation techniques using a few input utterances have been employed to overcome this problem. In this article, we survey these ad...

In an amplify-and-forward cooperative network, a closed-form expression of the a priori distribution of the complex-valued gain of the global relay channel is intractable, so that a priori information is often not exploited for estimating this gain. Here, we present two iterative channel gain and noise variance estimation algorithms that make use of a priori channel information and exploit the ...