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Memorias de investigación
Otras publicaciones:
Adaptive Sticky Generalized Metropolis
Año:2013
Áreas de investigación
  • Teoría de probabilidades y procesos estocásticos,
  • Inferencia de procesos estocásticos
Datos
Descripción
We introduce a new class of adaptive Metropolis algorithms called adaptive sticky algorithms for efficient general-purpose simulation from a target probability distribution. The transition of the Metropolis chain is based on a multiple-try scheme and the different proposals are generated by adaptive nonparametric distributions. Our adaptation strategy uses the interpolation of support points from the past history of the chain as in the adaptive rejection Metropolis. The algorithm efficiency is strengthened by a step that controls the evolution of the set of support points. This extra stage reduces the computational cost and accelerates the convergence of the proposal distribution to the target. Despite the algorithms are presented for univariate target distributions, we show that they can be easily extended to the multivariate context. We show the ergodicity of the proposed algorithms and illustrate their efficiency and effectiveness through some simulated examples involving target distributions with complex structures.
Internacional
No
Entidad
Arxiv
Lugar
Cornell University Library
Páginas
1 - 44
Referencia/URL
http://arxiv.org/abs/1308.3779
Tipo de publicación
Technical Report
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: David Luengo Garcia (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Ingeniería de Circuitos y Sistemas
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