It is well-known that Gaussian modeling of functional Magnetic Resonance Imaging (fMRI) magnitude time-course data, which are truly Rice-distributed, constitutes an approximation, especially at low signalto-noise ratios (SNRs). Based on this fact, previous work has argued that Rice-based activation tests show superior performance over their Gaussian-based counterparts at low SNRs and should be ...

An alternative to extrinsic information transfer (EXIT) charts called mean squared error (MSE) charts that use a measure related to the MSE instead of mutual information is proposed. Using the relationship between mutual information and minimum mean squared error (MMSE), a relationship between the rate of any code and the area under a plot of MSE versus signal to noise ratio (SNR) is obtained, ...

An alternative to extrinsic information transfer (EXIT) charts called mean squared error (MSE) charts that use a measure related to the MSE instead of mutual information is proposed. Using the relationship between mutual information and minimum mean squared error (MMSE), a relationship between the rate of any code and the area under a plot of MSE versus signal to noise ratio (SNR) is obtained, ...

A sequence of transforming functions is proposed to convert nongaussian distributions often seen in laboratory data to gaussian form. These transforms are chosen to eliminate or substantially reduce nongaussian characteristics of positive skewness and peakedness that result from two factors: (a) increases in variance with increasing mean values, and (b) general heterogeneity among intrapersonal...

In this paper we investigate alternative Lévy base correlation models that arise from the Gamma, Inverse Gaussian and CMY distribution classes. We compare these models to the basic (exponential) Lévy base correlation model and the classical Gaussian base correlation model. For all the investigated models, the Lévy base correlation curve is significantly flatter than the corresponding Gaussian o...

It is well-known that Gaussian modeling of functional Magnetic Resonance Imaging (fMRI) magnitude time-course data, which are truly Rice-distributed, constitutes an approximation, especially at low signal-to-noise ratios (SNRs). Based on this fact, previous work has argued that Rice-based activation tests show superior performance over their Gaussian-based counterparts at low SNRs and should be...

Radial Basis Networks are a reliable and efficient tool for performing classification tasks. In networks that include a Gaussian output transform within the Pattern Layer neurons, the method of setting the σ-width of the Gaussian curve is critical to obtaining accurate classification. Many existing methods perform poorly in regions of the problem space between examples of differing classes, or ...

On one hand we present below some examples of uid networks control which illustrate the general method for optimally emptying networks with linear holding costs proposed in Avram, Bertsimas and Ricard(95). On the other hand we attempt to study the changes in the optimal policies for uid models caused by stochasticity e ects. In the case of the usual expected total emptying cost objective, we fo...

PURPOSE To determine which machine learning classifier learns best to interpret standard automated perimetry (SAP) and to compare the best of the machine classifiers with the global indices of STATPAC 2 and with experts in glaucoma. METHODS Multilayer perceptrons (MLP), support vector machines (SVM), mixture of Gaussian (MoG), and mixture of generalized Gaussian (MGG) classifiers were trained...

In this paper we investigate one factor models that extend the classical Gaussian copula model for pricing CDOs. The proposed models are very tractable and perform significantly better than the classical Gaussian copula model. Moreover, we introduce the concept of Lévy base correlation. The obtained Lévy base correlation curve is much flatter than the corresponding Gaussian one. This indicates ...