Abstract
Among sequential Monte Carlo methods, sampling importance resampling (SIR) algorithms are based on importance sampling and on some (resampling-based) rejuvenation algorithm that aims at fighting against weight degeneracy. However, this mechanism tends to be insufficient when applied to informative or high-dimensional models. In this letter, we revisit the rejuvenation mechanism and propose a class of parameterized SIR-based solutions that enable us to adjust the tradeoff between computational cost and statistical performances.
| Original language | English |
|---|---|
| Pages (from-to) | 130-134 |
| Number of pages | 5 |
| Journal | IEEE Signal Processing Letters |
| Volume | 25 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Jan 2018 |
| Externally published | Yes |
Keywords
- Particle filters
- Resampling
- Sequential Monte Carlo algorithms
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