High-resolution numerical simulations of stationary inverse energy cascade in two-dimensional turbulence are presented. Deviations from Gaussian behavior of velocity differences statistics are quantitatively investigated. The level of statistical convergence is pushed enough to permit reliable measurement of the asymmetries in the probability distribution functions of longitudinal increments an...

In this paper we present a straightforward, efficient, and computationally fast method for creating a large number of discrete samples with an arbitrary given probability density function and a specified spectral content. The method relies on initially transforming a white noise sample set of random Gaussian distributed numbers into a corresponding set with the desired spectral distribution, af...

Given iid samples drawn from a distribution with known parametric form, we propose the minimization of expected Bregman divergence to form Bayesian estimates of differential entropy and relative entropy, and derive such estimators for the uniform, Gaussian, Wishart, and inverse Wishart distributions. Additionally, formulas are given for a log gamma Bregman divergence and the differential entrop...

In this paper we propose a feasible way to price American options in a model with time varying volatility and conditional skewness and leptokurtosis using GARCH processes and the Normal Inverse Gaussian distribution. We show how the risk neutral dynamics can be obtained in this model using the Generalized Local Risk Neutral Valuation Relationship of Duan (1999) and we derive approximation proce...

We consider the notion of canonical attacks, which are the cryptographic analog of the canonical forms of a one-mode Gaussian channel. Using this notion, we explore the connections between the degradability properties of the channel and its security for quantum key distribution. Finally, we also show some relations between canonical attacks and optimal Gaussian cloners.

We consider nonparametric Bayesian estimation inference using a rescaled smooth Gaussian field as a prior for a multidimensional function. The rescaling is achieved using a Gamma variable and the procedure can be viewed as choosing an inverse Gamma bandwidth. The procedure is studied from a frequentist perspective in three statistical settings involving replicated observations (density estimati...

In this paper, we introduce a new approach to estimate the subjective distribution of the future short rate from the historical dynamics of futures, based on a model generated by a Normal Inverse Gaussian distribution, with dynamical parameters. The model displays time varying conditional volatility, skewness and kurtosis and provides a flexible framework to recover the conditional distribution...

Geophysical inversion using least-squares has been a very successful method for UXO discrimination of magnetics. However, the residuals do not follow a normal distribution, which means that the assumptions underlying the inverse problem are violated. Two consequences of this are (i) model bias and (ii) incorrect estimates of model parameter uncertainties. We have found that the residuals are be...

Because conventional absorption models often fail to describe plasma concentration-time profiles following oral administration, empirical input functions such as the inverse Gaussian function have been successfully used. The purpose of this note is to extend this model by adding a first-order absorption process and to demonstrate the application of population analysis using maximum likelihood e...