Hierarchical Particle Filter
Hijerarhijski partikularni filter proširuje sekvencijalne Monte Karlo metode na modele stanja-prostora sa više nivoa latentnih promenljivih. Partikule se propagiraju na svakom nivou hijerarhije, omogućavajući metodi da istovremeno prati i detaljnu dinamiku stanja i sporije promenljive hiperparametre, dajući kalibrisane posteriorne raspodele na svim nivoima modela.
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Method map
The neighbourhood of related methods — select a node to explore.
Izvori
- Briers, M., Doucet, A. & Maskell, S. (2010). Smoothing algorithms for state-space models. Annals of the Institute of Statistical Mathematics, 62(1), 61-89. DOI: 10.1007/s10463-009-0236-2 ↗
- Chopin, N., Jacob, P. E. & Papaspiliopoulos, O. (2013). SMC2: an efficient algorithm for sequential analysis of state-space models. Journal of the Royal Statistical Society: Series B, 75(3), 397-426. DOI: 10.1111/j.1467-9868.2012.01046.x ↗
Kako citirati ovu stranicu
ScholarGate. (2026, June 3). Hierarchical Particle Filter. ScholarGate. https://scholargate.app/sr/bayesian/hierarchical-particle-filter
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Hierarchical Bayesian InferenceBajesovska statistika↔ compare
- Hijerarhijski Markovljevi lanci Monte KarlaBajesovska statistika↔ compare
- Kalmanov filterBajesovska statistika↔ compare
- Филтер честица (секвенцијални Монте Карло)Bajesovska statistika↔ compare
- Sekvenciјalni Monte KarloBajesovska statistika↔ compare
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