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Dynamic Metropolis-Hastings Algorithm/Ushahidi
Rekodi ya ushahidi wa mbinu

Dynamic Metropolis-Hastings Algorithm

The Dynamic Metropolis-Hastings (Dynamic MH) algorithm applies the Metropolis-Hastings MCMC sampler to Bayesian state-space and time-varying parameter models. At each time step, latent states or evolving parameters are updated via proposal-and-accept moves, yielding full posterior distributions over trajectories rather than single filtered estimates.

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Dynamic Metropolis-Hastings Algorithm for Time-Varying Models
Rekodi ya mbinu ya kiajenda · bayesian / bayesian
  • 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
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Taxonomic bucketDynamic Bayesian Inferencemachine-suggested · Relational suggestion, not evidence.Taxonomic bucketGibbs Samplingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketKalman Filtermachine-suggested · Relational suggestion, not evidence.Same method familyMetropolis-Hastings Algorithmmachine-suggested · Relational suggestion, not evidence.Same method familyParticle Filtermachine-suggested · Relational suggestion, not evidence.

Hali ya ushahidi

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

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