Algoritma Metropolis-Hastings Dinamis
Algoritma Metropolis-Hastings Dinamis (Dynamic MH) menerapkan sampler MCMC Metropolis-Hastings pada model keadaan-ruang Bayesian dan parameter yang berubah seiring waktu. Pada setiap langkah waktu, keadaan laten atau parameter yang berkembang diperbarui melalui langkah proposal-dan-penerimaan, menghasilkan distribusi posterior penuh atas lintasan daripada estimasi tersaring tunggal.
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Method map
The neighbourhood of related methods — select a node to explore.
Sumber
- 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 ↗
Cara menyitasi halaman ini
ScholarGate. (2026, June 3). Dynamic Metropolis-Hastings Algorithm for Time-Varying Models. ScholarGate. https://scholargate.app/id/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.
- Inferensi Bayesian DinamisBayesian↔ compare
- Sampling GibbsBayesian↔ compare
- Filter KalmanBayesian↔ compare
- Algoritma Metropolis-HastingsBayesian↔ compare
- Filter Partikel (Monte Carlo Sekuensial)Bayesian↔ compare
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