Dynamisk Metropolis-Hastings Algoritme
Den Dynamiske Metropolis-Hastings (Dynamisk MH) algoritme anvender Metropolis-Hastings MCMC sampleren på Bayesianske tilstandsrums- og tidsvarierende parameter-modeller. Ved hvert tidsskridt opdateres latente tilstande eller udviklende parametre via forslags-og-accept-trin, hvilket giver fulde posterior-fordelinger over trajektorier snarere end enkelte filtrerede estimater.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- 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 ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Dynamic Metropolis-Hastings Algorithm for Time-Varying Models. ScholarGate. https://scholargate.app/da/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.
- Dynamisk Bayesiansk InferensBayesiansk↔ compare
- Gibbs SamplingBayesiansk↔ compare
- Kalman-filterBayesiansk↔ compare
- Metropolis-Hastings AlgoritmenBayesiansk↔ compare
- Partikelfilter (sekventiel Monte Carlo)Bayesiansk↔ compare
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