Multichannel Satellite Retrieval of Cloud Parameter Probability Distribution Functions

Probability Distribution of the Order Parameter

The probability distribution of the order parameter is analyzed in order to obtain the criticality of magnetic systems. Monte Carlo simulations have been employed by using single spin flip Metropolis algorithm aided by finite-size scaling and histogram reweighting techniques. A method is proposed to obtain this probability distribution even when the transition temperature of the model is unknow...

Cloud Parameter Retrieval from Mipas Data

Clouds are a source of major uncertainty in climate models it is thus important to accurately model clouds in order to determine their properties. In this work, three cloud parameters (cloud top height, cloud top temperature and cloud extinction coefficient) are used to model the radiance measured in the MIPAS field-of-view as they represent the most obvious physical, thermodynamic and optical ...

On the Concavity of Multivariate Probability Distribution Functions on the Concavity of Multivariate Probability Distribution Functions

We prove that the multivariate standard normal probability distribution function is concave for large argument values. The method of proof allows for the derivation of similar statements for other types of multivariate probability distribution functions too. The result has important application, e.g., in probabilistic constrained stochastic programming problems.

Inferring Trading Strategies from Probability Distribution Functions Inferring Trading Strategies from Probability Distribution Functions

Bayesian Estimation of Shift Point in Shape Parameter of Inverse Gaussian Distribution Under Different Loss Functions

In this paper, a Bayesian approach is proposed for shift point detection in an inverse Gaussian distribution. In this study, the mean parameter of inverse Gaussian distribution is assumed to be constant and shift points in shape parameter is considered. First the posterior distribution of shape parameter is obtained. Then the Bayes estimators are derived under a class of priors and using variou...