29,859 research outputs found
Parameter selection for modeling of epidemic networks
The accurate modeling of epidemics on social contact networks is difficult due to the variation between different epidemics and the large number of parameters inherent to the problem. To reduce complexity, evolutionary computation is used to create a generative representation of the epidemic model. Previous gains from the use of local, verses global, operators are further explored to better balance exploration and exploitation of the genetic algorithm. A typical parameter study is conducted to test this new local operator and the new method of point packing is utilized as a proof of concept to perform a better search of the parameter space. All experiments from both approaches are tested against nine epidemic profiles. The point-packing driven parameter search demonstrates that the algorithm parameters interact substantially and in a non-linear fashion, and also shows that the good parameter settings are problem specific.Natural Sciences and Engineering Research Council of Canad
Robust Parameter Selection for Parallel Tempering
This paper describes an algorithm for selecting parameter values (e.g.
temperature values) at which to measure equilibrium properties with Parallel
Tempering Monte Carlo simulation. Simple approaches to choosing parameter
values can lead to poor equilibration of the simulation, especially for Ising
spin systems that undergo -order phase transitions. However, starting
from an initial set of parameter values, the careful, iterative respacing of
these values based on results with the previous set of values greatly improves
equilibration. Example spin systems presented here appear in the context of
Quantum Monte Carlo.Comment: Accepted in International Journal of Modern Physics C 2010,
http://www.worldscinet.com/ijmp
Statistical Analysis and Parameter Selection for Mapper
In this article, we study the question of the statistical convergence of the
1-dimensional Mapper to its continuous analogue, the Reeb graph. We show that
the Mapper is an optimal estimator of the Reeb graph, which gives, as a
byproduct, a method to automatically tune its parameters and compute confidence
regions on its topological features, such as its loops and flares. This allows
to circumvent the issue of testing a large grid of parameters and keeping the
most stable ones in the brute-force setting, which is widely used in
visualization, clustering and feature selection with the Mapper.Comment: Minor modification
Parameter selection in sparsity-driven SAR imaging
We consider a recently developed sparsity-driven synthetic aperture radar (SAR) imaging approach which can produce superresolution, feature-enhanced images. However, this regularization-based approach requires the selection of a hyper-parameter in order to generate such high-quality images. In this paper we present a number of techniques for automatically selecting the hyper-parameter
involved in this problem. In particular, we propose and develop numerical procedures for the use of Stein’s unbiased risk estimation, generalized cross-validation, and L-curve techniques for automatic parameter choice. We demonstrate and compare the effectiveness of these procedures through experiments based on both simple synthetic scenes, as well as electromagnetically simulated realistic data. Our results suggest that sparsity-driven SAR imaging coupled with the proposed automatic parameter choice procedures offers significant improvements over conventional SAR imaging
- …