Grid-based histogram arithmetic for the probabilistic analysis of functions

Carreras Vaquer, Carlos y Hermenegildo, Manuel V. (2000). Grid-based histogram arithmetic for the probabilistic analysis of functions. En: "4th International Symposium, SARA 2000", July 26-29, 2000, Horseshoe Bay, USA. ISBN 9783540678397.

Descripción

Título: Grid-based histogram arithmetic for the probabilistic analysis of functions
Autor/es:
  • Carreras Vaquer, Carlos
  • Hermenegildo, Manuel V.
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 4th International Symposium, SARA 2000
Fechas del Evento: July 26-29, 2000
Lugar del Evento: Horseshoe Bay, USA
Título del Libro: Abstraction, Reformulation, and Approximation
Fecha: 2000
ISBN: 9783540678397
Volumen: 1864
Materias:
Palabras Clave Informales: Interval computations, probabilistic analysis, estimation, approximate arithmetic, abstract interpretation, cálculo de intervalos, análisis probabilístico, estimación, aproximación aritmética, interpretación de resúmenes.
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The selection of predefined analytic grids (partitions of the numeric ranges) to represent input and output functions as histograms has been proposed as a mechanism of approximation in order to control the tradeoff between accuracy and computation times in several áreas ranging from simulation to constraint solving. In particular, the application of interval methods for probabilistic function characterization has been shown to have advantages over other methods based on the simulation of random samples. However, standard interval arithmetic has always been used for the computation steps. In this paper, we introduce an alternative approximate arithmetic aimed at controlling the cost of the interval operations. Its distinctive feature is that grids are taken into account by the operators. We apply the technique in the context of probability density functions in order to improve the accuracy of the probability estimates. Results show that this approach has advantages over existing approaches in some particular situations, although computation times tend to increase significantly when analyzing large functions.

Más información

ID de Registro: 14379
Identificador DC: http://oa.upm.es/14379/
Identificador OAI: oai:oa.upm.es:14379
URL Oficial: http://link.springer.com/chapter/10.1007%2F3-540-44914-0_7
Depositado por: Biblioteca Facultad de Informatica
Depositado el: 01 Feb 2013 08:11
Ultima Modificación: 21 Abr 2016 14:01
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