Correlations between parameters in risk models : estimation and propagation of uncertainty by Markov Chain Monte Carlo

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<dc:creator>Ades, A. E.</dc:creator>
<dc:creator>Lu, G.</dc:creator>
<dc:date>2003-12-01</dc:date>
<dc:description xml:lang="es">Monte Carlo simulation has become the accepted method for propagating parameter uncertainty through risk models. It is widely appreciated, however, that correlations between input variables must be taken into account if models are to deliver correct assessments of uncertainty in risk</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/56833.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Simulación Monte Carlo</dc:subject>
<dc:subject xml:lang="es">Dioxinas</dc:subject>
<dc:subject xml:lang="es">Análisis matemático</dc:subject>
<dc:subject xml:lang="es">Evaluación de riesgos</dc:subject>
<dc:subject xml:lang="es">Distribución de riesgos</dc:subject>
<dc:subject xml:lang="es">Modelos de simulación</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Correlations between parameters in risk models : estimation and propagation of uncertainty by Markov Chain Monte Carlo</dc:title>
<dc:relation xml:lang="es">En: Risk analysis : an international journal. - New York and London : Society for Risk Analysis. - Vol. 23, nº 6, December, 2003 ; p. 1165-1172</dc:relation>
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