It is well known that the probability distribution of stock returns is non-Gaussian. The tails of the distribution are too “fat,” meaning that extreme price movements, such as stock market crashes, occur more often than ...

In order to characterize quantum states within the context of information geometry, we propose a generalization of the Gaussian model, which we called the Hermite-Gaussian model. We obtain the Fisher-Rao metric and the ...

Discriminative training of Gaussian Mixture Models (GMMs) for speech or speaker recognition purposes is usually based on the gradient descent method, in which the iteration step-size, ε, uses to be defined experimentally. ...

This work deals with the joint simulation of copper grade (as a continuous regionalized variable) and rock type (as a categorical variable) in Lince–Estefanía deposit, located in northern Chile. The region under study ...

Conventional reliability analysis techniques are focused on the occurrence of failures over time. However, in certain situations where the occurrence of failures is tiny or almost null, the estimation of the quantities ...

In this article the application of a geostatistical method based on Gaussian simulation is studied to reduce the uncertainty of atypical data related to the hydrogeological data of a quarry, for which the data of the ...

The purpose of the Graphical Gaussian model is to find the covariance structure that represents the relationship between random variables, whose joint distribution is a multivariate normal. This is a tool used to modeling ...

Discriminative training of Gaussian Mixture Models (GMMs) for speech or speaker recognition purposes is usually based on the gradient descent method, in which the iteration step-size, ε, uses to be defined experimentally. ...