Solomonoff’s uncomputable universal prediction scheme ξ allows to predict the next symbol xk of a sequence x1...xk−1 for any Turing computable, but otherwise unknown, probabilistic environment μ. This scheme will be generalized to arbitrary environmental classes, which, among others, allows the construction of computable universal prediction schemes ξ. Convergence of ξ to μ in a conditional mea...

We propose several cost functions for registration of shapes encoded with Euclidean and/or nonEuclidean information (unit vectors). Our framework is assessed for estimation of both rigid and non-rigid transformations between the target and model shapes corresponding to 2D contours and 3D surfaces. The experimental results obtained confirm that using the combination of a point’s position and uni...

In this paper, we consider a simple preliminary-test estimation problem where the analyst’s loss structure is represented by a ‘reflected Normal’ penalty function. In particular we consider the estimation of the location parameter in a Normal sampling problem, where a preliminary test is conducted for the validity of a simple restriction on this parameter. The exact finite-sample risk of this p...

In this paper, we describe a new vector similarity measure associated with a convex cost function. Given two vectors, we determine the surface normals of the convex function at the vectors. The angle between the two surface normals is the similarity measure. Convex cost function can be the negative entropy function, total variation (TV) function and filtered variation function. The convex cost ...

Let be a random sample from a normal distribution with unknown mean and known variance The usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. In many practical situations, is known in advance to lie in an interval, say for some In this case, the maximum likelihood estimator...

let be a random sample from a normal distribution with unknown mean and known variance the usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. in many practical situations, is known in advance to lie in an interval, say for some in this case, the maximum likelihood estimator changes and d...

This paper considers estimation of normal mean ? when the variance is unknown, using the LINEX loss function. The unique Bayes estimate of ? is obtained when the precision parameter has an Inverse Gaussian prior density

OBJECTIVE To determine appropriate counseling of patients with olfactory dysfunction. DESIGN Retrospective analysis. SETTING Interdisciplinary Center for Smell and Taste, University of Dresden Medical School, Technical University of Dresden. PATIENTS A total of 361 males and 533 females, aged 11 to 84 years, who twice reported to the Interdisciplinary Center for Smell and Taste. MAIN OU...

introduction: glaucoma is one of the leading causes of blindness throughout the world. some studies have suggested a relationship between glaucoma and sensorineural hearing loss, while others have found no evidence of an association. we performed a study to determine whether there is a significant difference in hearing of patients with glaucoma and a match control population. methods: in this c...