estimation of directional vectors, based on joint diagonalization of 4th-order cumulant matrices

, the posterior pdf is approximated by a diagonal Gaussian pdf. According to the ICA-model used in this paper, the measurements are generated by a linear mapping from mutually independent source signals whose distributions are mixtures of Gaussians. The measurements are also assumed to have additive Gaussian

[i], then the Diagonal Gaussian graph generated by α, G 8 α = (V, E), is defined as:

n K . Assembling sample and image values in vectors, image reconstruction may be rewritten in matrix notation: With such linear mapping the propagation of noise from sample values into image values is conveniently described by noise matrices. The -th diagonal entry of the image noise matrix X

of different food and liquid rewards Reward neurons should distinguish rewards from punishers. Different neurons in orbitofrontal cortex respond to rewarding and aversive liquids The omission of reward following a CS moves the contingency toward the diagonal line in Prediction Error Just as with behavioral

of edge-wise partial correlation coefficients. This representation holds for a large class of Gaussian graphical models which we call walk-summable. We give a precise characterization of this class of models, and relate it to other classes including diagonally dominant, attractive, nonfrustrated

and we show how they can be optimized, off-line or on-line, by simple yet remarkably efficient algorithms (one is based on a novel joint diagonalization procedure, the other on a Newton-like technique). The paper also includes (limited) numerical experiments and a discussion contrasting non-Gaussian

Abstract. Let B ∈ Mn(C) be a row diagonally dominant matrix, i.e., n� σi|bii | = |bij|, i =1,...,n, j=1 j�=i where 0 ≤ σi < 1, i =1,...,n, with σ =max1≤i≤n σi. We show that no pivoting is necessary when Gaussian elimination is applied to A = B −1. Moreover, the growth factor for A does

). The resulting non-Gaussian bounds describe simultaneously both the case of bounded domains and the case, modelled on the half-space example, of domains which satisfy a twisted infinite internal cone condition. Bounds for the Green’s function are given as well.

expensive to evaluate all the Gaussians of the mixture density codebook, many recognizers only compute the M most significant Gaussians (M = 1; : : : ; 8). This paper presents an alternative approach to approximate mixture Gaussians with diagonal covariance matrices, based on a binary feature space