Consider a Bayesian optimal design with many support points which poses the problem of collecting data with a few number of observations at each design point. Under such a scenario the asymptotic property of using Fisher information matrix for approximating the covariance matrix of posterior ML estimators might be doubtful. We suggest to use Bhattcharyya matrix in deriving the information matri...

Non-quadratic regularizers, in particular the ` 1 norm regularizer can yield sparse solutions that generalize well. In this work we propose the Generalized Subspace Information Criterion (GSIC) that allows to predict the generalization error for this useful family of regularizers. We show that under some technical assumptions GSIC is an asymptotically unbiased estimator of the generalization er...

However, for all the significance and widespread prevalence of stress among the population, there is still no appliances or devices to measure the level of stress in people in real everyday life Modern medical and biological and psychophysiological studies convincingly show that emotional stress is the cause of many psychosomatic diseases, exerts a comprehensive destructive influence on the vit...

This paper presents an effective method for clustering unknown speech utterances based on their associated speakers. The proposed method jointly optimizes the generated clusters and the number of clusters according to a Bayesian information criterion (BIC). The criterion assesses a partitioning of utterances based on how high the level of withincluster homogeneity can be achieved at the expense...

. This paper proposes a new complexity-penalization model selection strategy derived from the minimum risk principle and the behavior of candidate models under noisy conditions. This strategy seems to be robust in small sample size conditions and tends to AIC criterion as sample size grows up. The simulation study at the end of the paper will show that the proposed criterion is extremely compet...