Dinamički Metropolis-Hastingsov algoritam
Dinamički Metropolis-Hastingsov (Dynamic MH) algoritam primjenjuje Metropolis-Hastingsov MCMC uzorkivač na Bayesove modele prostora stanja i parametara koji se mijenjaju s vremenom. U svakom vremenskom koraku, latentna stanja ili parametri koji evoluiraju ažuriraju se potezima prijedloga i prihvaćanja, dajući potpune posteriorne raspodjele preko putanja, a ne pojedinačnih filtriranih procjena.
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Method map
The neighbourhood of related methods — select a node to explore.
Izvori
- Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109. DOI: 10.1093/biomet/57.1.97 ↗
- Carlin, B. P., Polson, N. G., & Stoffer, D. S. (1992). A Monte Carlo approach to nonnormal and nonlinear state-space modeling. Journal of the American Statistical Association, 87(418), 493–500. DOI: 10.1080/01621459.1992.10475231 ↗
Kako citirati ovu stranicu
ScholarGate. (2026, June 3). Dynamic Metropolis-Hastings Algorithm for Time-Varying Models. ScholarGate. https://scholargate.app/hr/bayesian/dynamic-metropolis-hastings-algorithm
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.
- Dinamičko Bayesijansko zaključivanjeBayesovska statistika↔ compare
- Gibbs uzorkovanjeBayesovska statistika↔ compare
- Kalmanov filtarBayesovska statistika↔ compare
- Metropolis-Hastingsov algoritamBayesovska statistika↔ compare
- Particle Filter (Sequential Monte Carlo)Bayesovska statistika↔ compare
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