The Generalized Splitting Method: Applications to Combinatorial Counting and Static Rare-Event Probability Estimation - Couverture souple

Botev, Zdravko

 
9783838397269: The Generalized Splitting Method: Applications to Combinatorial Counting and Static Rare-Event Probability Estimation
Couverture souple
ISBN 10 : 3838397266 ISBN 13 : 9783838397269
Editeur : LAP LAMBERT Academic Publishing, 2010

Synopsis

We describe a new Monte Carlo algorithm for the consistent and unbiased estimation of multidimensional integrals and the efficient sampling from multidimensional densities. The algorithm is inspired by the classical splitting method and can be applied to general static simulation models. We provide examples from rare-event probability estimation, counting, and sampling, demonstrating that the proposed method can outperform existing Markov chain sampling methods in terms of convergence speed and accuracy. The second part of the thesis presents a new adaptive kernel density estimator based on linear diffusion processes. The proposed estimator builds on existing ideas for adaptive smoothing by incorporating information from a pilot density estimate. In addition, we propose a new plug- in bandwidth selection method that is free from the arbitrary normal reference rules used by existing methods. We present simulation examples in which the proposed approach outperforms existing methods in terms of accuracy and reliability.

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Présentation de l'éditeur

We describe a new Monte Carlo algorithm for the consistent and unbiased estimation of multidimensional integrals and the efficient sampling from multidimensional densities. The algorithm is inspired by the classical splitting method and can be applied to general static simulation models. We provide examples from rare-event probability estimation, counting, and sampling, demonstrating that the proposed method can outperform existing Markov chain sampling methods in terms of convergence speed and accuracy. The second part of the thesis presents a new adaptive kernel density estimator based on linear diffusion processes. The proposed estimator builds on existing ideas for adaptive smoothing by incorporating information from a pilot density estimate. In addition, we propose a new plug- in bandwidth selection method that is free from the arbitrary normal reference rules used by existing methods. We present simulation examples in which the proposed approach outperforms existing methods in terms of accuracy and reliability.

Biographie de l'auteur

Dr. Botev has graduated from the University of Queensland, Australia with First class Honors degree and a university medal in 2005. He completed his Phd in 2009 and since then he has lectured at the University of Queensland. Currently, he is a postdoctoral fellow at the University of Montreal.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2010
Langue
anglais
ISBN 10 
3838397266
ISBN 13 
9783838397269
Reliure
Broché
Nombre de pages
140
Coordonnées du fabricant
non disponible
Personne responsable
non disponible