The paper considers model selection in regression under the additional structural constraints on admissible models where the number of potential predictors miht be even larger than the available sample size. We develop a Bayesian formalism which is used as a natural tool for generating a wide class of model selection criteria based on penalized least squares estimation with various complexity p...

In this paper, we present an Extended Kalman Filter (EKF)-based estimator for simultaneous localization and mapping (SLAM) with processing requirements that are linear in the number of features in the map. The proposed algorithm, called the Power SLAM, is based on three key ideas. Firstly, by introducing the Global Map Postponement method, approximations necessary for ensuring linear computatio...

We present an algorithm to reconstruct gray scale images corrupted by noise. We use a Bayesian approach. The unknown original image is assumed to be a realization of a Markov random field on a finite two dimensional region Λ ⊂ Z2. This image is degraded by some noise, which is assumed to act independently in each site of Λ and to have the same distribution on all sites. For the estimator we use...

The connection between Bayesian statistics and the technique of regularization for inverse problems has been given significant attention in recent years. For example, Bayes’ law is frequently used as motivation for variational regularization methods of Tikhonov type. In this setting, the regularization function corresponds to the negative-log of the prior probability density; the fit-to-data fu...

Community detection in networks has drawn much attention in diverse fields, especially social sciences. Given its significance, there has been a large body of literature among which many are not statistically based. In this paper, we propose a novel stochastic blockmodel based on a logistic regression setup with node correction terms to better address this problem. We follow a Bayesian approach...

In this work, we describe a procedure to visualize nonlinear process dynamics using a self-organizing map based local model dynamical estimator. The proposed method exploits the topology preserving nature of the resulting estimator to extract visualizations (planes) of insightful dynamical features, that allow to explore nonlinear systems whose behavior changes with the operating point. Since t...

Channel estimation is an important prerequisite for receiver design. In this paper we present a semi-blind low complexity frequency domain based channel estimation algorithm for multi-access Orthogonal Frequency Division Multiplexing (OFDM) systems. Our algorithm is based on eigenvalues interpolation and makes a collective use of data and channel constraints. We exploit these constraints to der...

Here we obtain approximate Bayes inferences through variational methods when an exponential power family type prior is specified for the regression coefficients to mimic the characteristics of the Bridge regression. We accomplish this through hierarchical modeling of such priors. Although the mixing distribution is not explicitly stated for scale normal mixtures, we obtain the required moments ...

This paper discusses the localization portion of a new iceberg-relative navigation technique for Autonomous Underwater Vehicles (AUVs). An estimator is presented which correlates incoming sonar range returns with an a-priori iceberg surface map in order to provide iceberg-relative AUV position and orientation estimates. The technique works by maintaining estimates not only of iceberg-relative A...

In this paper, the problem of joint oscillator phase noise (PHN) estimation and data detection for multi-input multi-output (MIMO) systems using bit-interleaved coded modulation (BICM) is analyzed. A new MIMO receiver that iterates between the estimator and the detector, based on the expectation-maximization (EM) framework, is proposed. It is shown that at high signal-to-noise ratios, a maximum...