On the convergence of two sequential Monte Carlo methods for maximum a posteriori sequence estimation and stochastic global optimization

DOI10.1007/s11222-011-9294-4zbMath1322.65004OpenAlexW1993585713MaRDI QIDQ746254

Dan Crisan, Joaquín Míguez, Petar M. Djurić

Publication date: 16 October 2015

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-011-9294-4

zbMATH Keywords

global optimizationstate space modelssequential Monte Carloconvergence of particle filtersMAP sequence estimation

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Monte Carlo methods (65C05) Sequential estimation (62L12)

Related Items (11)

Properties of marginal sequential Monte Carlo methods ⋮ The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems ⋮ A Particle Method for Solving Fredholm Equations of the First Kind ⋮ Nested particle filters for online parameter estimation in discrete-time state-space Markov models ⋮ Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization ⋮ A Rao-blackwellized particle filter for joint parameter estimation and biomass tracking in a stochastic predator-prey system ⋮ Particle-kernel estimation of the filter density in state-space models ⋮ Importance sampling: intrinsic dimension and computational cost ⋮ Sequential Monte Carlo methods for filtering of unobservable components of multidimensional diffusion Markov processes ⋮ Nudging the particle filter ⋮ On the performance of particle filters with adaptive number of particles

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