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Bayesian methods

Algoriti ya Metropolis-Hastings

Algoriti ya Metropolis-Hastings (MH) ni mbinu ya jumla ya mnyororo wa Markov Monte Carlo (MCMC) kwa ajili ya kupata sampuli kutoka kwa usambazaji wowote wa uwezekano ambao msongamano wake unaweza kutathminiwa hadi kwa kiwango cha kawaida. Ilianzishwa na Metropolis, Rosenbluth, Rosenbluth, Teller, na Teller (1953) katika fizikia ya kompyuta na kuendelezwa na Hastings (1970) kwa usambazaji wa mapendekezo yasiyo sawia, ni algoriti ya msingi ambayo karibu sampula zote za baadaye za MCMC — sampuli ya Gibbs, Hamiltonian Monte Carlo, sampuli ya mchoro — zinatokana au zinaweza kuonekana kama visa maalum.

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Vyanzo

  1. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092. DOI: 10.1063/1.1699114
  2. 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
  3. Robert, C. P., & Casella, G. (2004). Monte Carlo Statistical Methods (2nd ed.). Springer. ISBN: 978-0-387-21239-5
  4. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1-439-84095-5

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Metropolis-Hastings Markov Chain Monte Carlo Algorithm. ScholarGate. https://scholargate.app/sw/bayesian/metropolis-hastings-algorithm

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Imerejelewa na

ScholarGateMetropolis-Hastings Algorithm (Metropolis-Hastings Markov Chain Monte Carlo Algorithm). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/bayesian/metropolis-hastings-algorithm · Seti ya data: https://doi.org/10.5281/zenodo.20539026