The field of sequential Monte Carlo methods

Katarzyna Brzozowska–Rup, and Antoni Leon Dawodowicz. "The field of sequential Monte Carlo methods." Mathematica Applicanda 37.51/10 (2009): null. <http://eudml.org/doc/292918>.

@article{KatarzynaBrzozowska2009,
abstract = {This paper provides an introduction to the field of sequential Monte Carlo methods which are also known as particle filters methods. The best known algorithm to solve the problem of non-linear non-Gaussian filtering is the Extended Kalman Filter (EKF) but in settings where the dynamics are significantly non-linear or the noise intensities are high, the EKF can perform quite poorly. Particle filtering methods are powerful tools for online estimation and tracking in nonlinear and non-Gaussian dynamical systems. The basic idea is to approximate the transition probability density function by a discrete cloud of points, called particles. They commonly consistof three steps:(1) drawing samples in the state-space of the system,(2) computing proper importance weights of each sample and(3) resampling.These methods are becoming increasingly popular in economics and finance so the objective of this paper is to explain the basic assumptions of the methodology and provide references to relevant literature.},
author = {Katarzyna Brzozowska–Rup, Antoni Leon Dawodowicz},
journal = {Mathematica Applicanda},
keywords = {state-space models, hiddenMarkov model, optimal filtering, sequential Monte Carlo, sequential importance sampling, resampling.},
language = {eng},
number = {51/10},
pages = {null},
title = {The field of sequential Monte Carlo methods},
url = {http://eudml.org/doc/292918},
volume = {37},
year = {2009},
}

TY - JOUR
AU - Katarzyna Brzozowska–Rup
AU - Antoni Leon Dawodowicz
TI - The field of sequential Monte Carlo methods
JO - Mathematica Applicanda
PY - 2009
VL - 37
IS - 51/10
SP - null
AB - This paper provides an introduction to the field of sequential Monte Carlo methods which are also known as particle filters methods. The best known algorithm to solve the problem of non-linear non-Gaussian filtering is the Extended Kalman Filter (EKF) but in settings where the dynamics are significantly non-linear or the noise intensities are high, the EKF can perform quite poorly. Particle filtering methods are powerful tools for online estimation and tracking in nonlinear and non-Gaussian dynamical systems. The basic idea is to approximate the transition probability density function by a discrete cloud of points, called particles. They commonly consistof three steps:(1) drawing samples in the state-space of the system,(2) computing proper importance weights of each sample and(3) resampling.These methods are becoming increasingly popular in economics and finance so the objective of this paper is to explain the basic assumptions of the methodology and provide references to relevant literature.
LA - eng
KW - state-space models, hiddenMarkov model, optimal filtering, sequential Monte Carlo, sequential importance sampling, resampling.
UR - http://eudml.org/doc/292918
ER -