TY - GEN
T1 - An improved SIR-based Sequential Monte Carlo algorithm
AU - Lamberti, Roland
AU - Petetin, Yohan
AU - Septier, Francois
AU - Desbouvries, Francois
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/24
Y1 - 2016/8/24
N2 - Sequential Monte Carlo (SMC) algorithms are based on importance sampling (IS) techniques. Resampling has been introduced as a tool for fighting the weight degeneracy problem. However, for a fixed sample size N, the resampled particles are dependent, are not drawn exactly from the target distribution, nor are weighted properly. In this paper, we revisit the resampling mechanism and propose a scheme where the resampled particles are (conditionally) independent and weighted properly. We validate our results via simulations.
AB - Sequential Monte Carlo (SMC) algorithms are based on importance sampling (IS) techniques. Resampling has been introduced as a tool for fighting the weight degeneracy problem. However, for a fixed sample size N, the resampled particles are dependent, are not drawn exactly from the target distribution, nor are weighted properly. In this paper, we revisit the resampling mechanism and propose a scheme where the resampled particles are (conditionally) independent and weighted properly. We validate our results via simulations.
KW - Importance sampling
KW - resampling procedures
KW - sequential Monte Carlo
U2 - 10.1109/SSP.2016.7551745
DO - 10.1109/SSP.2016.7551745
M3 - Conference contribution
AN - SCOPUS:84987909399
T3 - IEEE Workshop on Statistical Signal Processing Proceedings
BT - 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016
PB - IEEE Computer Society
T2 - 19th IEEE Statistical Signal Processing Workshop, SSP 2016
Y2 - 25 June 2016 through 29 June 2016
ER -