Computational aspects of sequential Monte Carlo filter and smoother

DOI10.1007/s10463-014-0446-0zbMath1334.65025OpenAlexW2097627707MaRDI QIDQ457255

Genshiro Kitagawa

Publication date: 26 September 2014

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-014-0446-0

zbMATH Keywords

parallel computationparticle filterGaussian-sum filternonlinear non-Gaussian state-space modelposterior mean smoothertwo-filter formula

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Monte Carlo methods (65C05)

Related Items

The auxiliary iterated extended Kalman particle filter, RMCMC: a system for updating Bayesian models, On particle methods for parameter estimation in state-space models

Uses Software

• MersenneTwister

Cites Work

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• Smoothing algorithms for state-space models
• Bayesian forecasting and dynamic models
• Non-Gaussian seasonal adjustment
• The two-filter formula for smoothing and an implementation of the Gaussian-sum smoother
• Smoothness priors analysis of time series
• Sequential Monte Carlo Methods in Practice
• Introduction to Time Series Modeling
• A sequential smoothing algorithm with linear computational cost
• Non-Gaussian State-Space Modeling of Nonstationary Time Series
• Smoothing estimation of stochastic processes: Two-filter formulas
• Mersenne twister
• Monte carlo filter using the genetic algorithm operators
• Filtering via Simulation: Auxiliary Particle Filters
• Nonlinear Bayesian estimation using Gaussian sum approximations