Fast perfect weighted resampling

We describe an algorithm for perfect weighted-random resampling of a population with time complexity O(m + n) for resampling to inputs to produce n outputs. This algorithm is an incremental improvement over standard resampling algorithms. Our resampling algorithm is parallelizable, with linear speed...

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Bibliographic Details
Published in2008 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 3457 - 3460
Main Author Massey, B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2008
Subjects
Band pass filters
Bayesian methods
Computer science
Costs
Filtering
Monte Carlo methods
Particle tracking
Sampling methods
Sensor fusion
State estimation
State-space methods
tracking filters
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Summary:We describe an algorithm for perfect weighted-random resampling of a population with time complexity O(m + n) for resampling to inputs to produce n outputs. This algorithm is an incremental improvement over standard resampling algorithms. Our resampling algorithm is parallelizable, with linear speedup. Linear-time resampling yields notable performance improvements in our motivating example of sequential importance resampling for Bayesian particle filtering.
ISBN:9781424414833
1424414830
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2008.4518395