Quantitative approximations of evolving probability measures and sequential Markov chain Monte Carlo methods

We study approximations of evolving probability measures by an interacting particle system. The particle system dynamics is a combination of independent Markov chain moves and importance sampling/resampling steps. Under global regularity conditions, we derive non-asymptotic error bounds for the part...

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Veröffentlicht in:	Probability theory and related fields 2013-04, Vol.155 (3-4), p.665-701
Hauptverfasser:	Eberle, Andreas, Marinelli, Carlo
Format:	Artikel
Sprache:	eng
Schlagworte:	Approximation Chain mobility Constants Dynamical systems Dynamics Economics Evolution Finance Importance sampling Insurance Management Markov analysis Markov chains Mathematical and Computational Biology Mathematical and Computational Physics Mathematics Mathematics and Statistics Monte Carlo methods Monte Carlo simulation Operations Research/Decision Theory Probability Probability Theory and Stochastic Processes Quantitative Finance Regularity Resampling Statistics for Business System dynamics Theoretical
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