Search results for "Gauss"

Blood vessels and feature points detection on retinal images

2009

In this paper we present a method for the automatic extraction of blood vessels from retinal images, while capturing points of intersection/overlap and endpoints of the vascular tree. The algorithm performance is evaluated through a comparison with handmade segmented images available on the STARE project database (STructured Analysis of the REtina). The algorithm is performed on the green channel of the RGB triad. The green channel can be used to represent the illumination component. The matched filter is used to enhance vessels w.r.t. the background. The separation between vessels and background is accomplished by a threshold operator based on gaussian probability density function. The len…

Composite laminates buckling optimization through Levy based Ant Colony Optimization

2010

In this paper, the authors propose the use of the Levy probability distribution as leading mechanism for solutions differentiation in an efficient and bio-inspired optimization algorithm, ant colony optimization in continuous domains, ACOR. In the classical ACOR, new solutions are constructed starting from one solution, selected from an archive, where Gaussian distribution is used for parameter diversification. In the proposed approach, the Levy probability distributions are properly introduced in the solution construction step, in order to couple the ACOR algorithm with the exploration properties of the Levy distribution. The proposed approach has been tested on mathematical test functions…

ADVANCED MESHLESS NUMERICAL METHODS AND APPLICATIONS

Identifying modularity structure of a genetic network in gene expression profile data

2009

Aim of this paper is to define a new statistical framework to identify central modules in Gaussian Graphical Models (GGMs) estimated by gene expression data measured on a sample of patients with negative molecular response to Imatinib. Imanitib is a drug used to treat certain types of cancer that in many statistical studies has been reported to have a significant clinical effect on chronic myeloid leukemia (CML) in chronic phase as well as in blast crisis. For central module in a GGM we intend a module containing genes that are defined differently expressed.

On VaR using modified gaussian copula

2008

The problem of modeling asset returns is one of the most important issue in finance. People generally use Gaussian processes because of their tractable properties for computation. However, it is well known that asset returns are fat-tailed leading to an underestimation of the risk. One of the most recent proposals is to model the interdependence of asset returns, for example in a portfolio, by means of Copulas and choose marginal distributions with fat tail to fit the single asset returns. The aim of the paper is to show first results concerning the evaluation of Portfolio Value-at-Risk (VaR) using the Gaussian copula, modified by introducing a particular correlation coefficient, and assumi…

Discrete Time Portfolio Selection with Lévy Processes

2007

This paper analyzes discrete time portfolio selection models with Lévy processes. We first implement portfolio models under the hypotheses the vector of log-returns follow or a multivariate Variance Gamma model or a Multivariate Normal Inverse Gaussian model or a Brownian Motion. In particular, we propose an ex-ante and an ex-post empirical comparisons by the point of view of different investors. Thus, we compare portfolio strategies considering different term structure scenarios and different distributional assumptions when unlimited short sales are allowed.

A Comparison among Portfolio Selection Strategies with Subordinated Lévy Processes

2007

In this paper we describe portfolio selection models using Lévy processes. The contribution consists in comparing some portfolio selection strategies under different distributional assumptions. We first implement portfolio models under the hypothesis the log-returns follow a particular process with independent and stationary increments. Then we compare the ex-post final wealth of optimal portfolio selection models with subordinated Lévy processes when limited short sales and transaction costs are allowed.

Probabilistic response of linear structures equipped with nonlinear dampers devices (PIS method)

2008

Passive control introducing energy absorbing devices into the structure has received considerable attention in recent years. Unfortunately the constitutive law of viscous fluid dampers is highly nonlinear, and even supposing that the structure behaves linearly, the whole system has inherent nonlinear properties. Usually the analysis is performed by a stochastic linearization technique (SLT) determining a linear system equivalent to the nonlinear one, in a statistical sense. In this paper the effect of the non-Gaussianity of the response due to the inherent nonlinearity of the damper device will be studied in detail via the Path Integral Solution (PIS) method. A systematic study is conducted…

Statistical Learning for End-to-End Simulations

2018

End-to-end mission performance simulators (E2ES) are suitable tools to accelerate satellite mission development from concet to deployment. One core element of these E2ES is the generation of synthetic scenes that are observed by the various instruments of an Earth Observation mission. The generation of these scenes rely on Radiative Transfer Models (RTM) for the simulation of light interaction with the Earth surface and atmosphere. However, the execution of advanced RTMs is impractical due to their large computation burden. Classical interpolation and statistical emulation methods of pre-computed Look-Up Tables (LUT) are therefore common practice to generate synthetic scenes in a reasonable…

Physics-aware Gaussian processes in remote sensing

2018

Abstract Earth observation from satellite sensory data poses challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression has excelled in biophysical parameter estimation tasks from airborne and satellite observations. GP regression is based on solid Bayesian statistics, and generally yields efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations between the state vector and the radiance observations is available though and could be useful to improve pre…