DTSC - GTSA - Artículos de Revistashttp://hdl.handle.net/10016/90422016-10-27T09:06:12Z2016-10-27T09:06:12ZSupporting scientific knowledge discovery with extended, generalized Formal Concept AnalysisValverde Albacete, Francisco JoséGonzález Calabozo, Jose MaríaPeñas, AnselmoPeláez Moreno, Carmenhttp://hdl.handle.net/10016/236622016-09-29T10:54:41Z2016-02-01T00:00:00ZSupporting scientific knowledge discovery with extended, generalized Formal Concept Analysis
Valverde Albacete, Francisco José; González Calabozo, Jose María; Peñas, Anselmo; Peláez Moreno, Carmen
In this paper we fuse together the Landscapes of Knowledge of Wille's and Exploratory Data Analysis by leveraging Formal Concept Analysis (FCA) to support data-induced scientific enquiry and discovery. We use extended FCA first by allowing K-valued entries in the incidence to accommodate other, non-binary types of data, and second with different modes of creating formal concepts to accommodate diverse conceptualizing phenomena. With these extensions we demonstrate the versatility of the Landscapes of Knowledge metaphor to help in creating new scientific and engineering knowledge by providing several successful use cases of our techniques that support scientific hypothesis-making and discovery in a range of domains: semiring theory, perceptual studies, natural language semantics, and gene expression data analysis. While doing so, we also capture the affordances that justify the use of FCA and its extensions in scientific discovery.
2016-02-01T00:00:00ZEfficient random variable generation: ratio of uniforms and polar rejection samplingLuengo García, DavidMartino, Lucahttp://hdl.handle.net/10016/166452013-10-01T00:03:58Z2012-03-01T00:00:00ZEfficient random variable generation: ratio of uniforms and polar rejection sampling
Luengo García, David; Martino, Luca
Monte Carlo techniques, which require the generation of samples from some target density, are often the only alternative for performing Bayesian inference. Two classic sampling techniques to draw independent samples are the ratio of uniforms (RoU) and rejection sampling (RS). An efficient sampling algorithm is proposed combining the RoU and polar RS (i.e. RS inside a sector of a circle using polar coordinates). Its efficiency is shown in drawing samples from truncated Cauchy and Gaussian random variables, which have many important applications in signal processing and communications.
2012-03-01T00:00:00ZEfficient sampling from truncated bivariate Gaussians via Box-Muller transformationMartino, LucaLuengo García, DavidMíguez Arenas, Joaquínhttp://hdl.handle.net/10016/166442013-10-01T00:05:07Z2012-11-01T00:00:00ZEfficient sampling from truncated bivariate Gaussians via Box-Muller transformation
Martino, Luca; Luengo García, David; Míguez Arenas, Joaquín
Many practical simulation tasks demand procedures to draw samples efficiently from multivariate truncated Gaussian distributions. Introduced is a novel rejection approach, based on the Box-Muller transformation, to generate samples from a truncated bivariate Gaussian density with an arbitrary support. Furthermore, for an important class of support regions the new method allows exact sampling to be achieved, thus becoming the most efficient approach possible.Many practical simulation tasks demand procedures to draw samples efficiently from multivariate truncated Gaussian distributions. Introduced is a novel rejection approach, based on the Box-Muller transformation, to generate samples from a truncated bivariate Gaussian density with an arbitrary support. Furthermore, for an important class of support regions the new method allows exact sampling to be achieved, thus becoming the most efficient approach possible.
2012-11-01T00:00:00ZAlmost rejectionless sampling from Nakagami-m distributions (m≥1)Luengo García, DavidMartino, Lucahttp://hdl.handle.net/10016/166432013-10-01T00:06:38Z2012-11-01T00:00:00ZAlmost rejectionless sampling from Nakagami-m distributions (m≥1)
Luengo García, David; Martino, Luca
The Nakagami-m distribution is widely used for the simulation of fading channels in wireless communications. A novel, simple and extremely efficient acceptance-rejection algorithm is introduced for the generation of independent Nakagami-m random variables. The proposed method uses another Nakagami density with a half-integer value of the fading parameter, mp=n/2=m, as proposal function, from which samples can be drawn exactly and easily. This novel rejection technique is able to work with arbitrary values of m=1, average path energy, =, and provides a higher acceptance rate than all currently available methods.
2012-11-01T00:00:00Z