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Bayesian methods

Utoaji wa Kigezo

Utoaji wa kigezo (VI) ni familia ya mbinu zinazobadilisha ukokotoaji wa baada wa Bayesian kuwa tatizo la uboreshaji. Badala ya kuchora sampuli kutoka kwa baada halisi — kama vile Markov chain Monte Carlo inavyofanya — VI huweka familia rahisi, inayoweza kushughulikiwa ya usambazaji na hupata mwanachama wa familia hiyo aliye karibu zaidi na baada halisi kwa kuongeza kikomo cha chini cha ushahidi (ELBO). Ilianzishwa katika mfumo wake wa kisasa wa kielelezo cha kielelezo na Jordan, Ghahramani, Jaakkola na Saul (1999) na kupewa matibabu kamili ya takwimu na Blei, Kucukelbir na McAuliffe (2017), VI sasa ni injini ya kawaida ya utoaji inayoweza kupimika katika ujifunzaji wa mashine wa uwezekano.

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Vyanzo

  1. Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233. DOI: 10.1023/A:1007665907178
  2. Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859–877. DOI: 10.1080/01621459.2017.1285773
  3. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. (Chapter 10: Approximate Inference.) ISBN: 978-0387310732

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Variational Bayesian Inference. ScholarGate. https://scholargate.app/sw/bayesian/variational-inference

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Imerejelewa na

ScholarGateVariational Inference (Variational Bayesian Inference). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/bayesian/variational-inference · Seti ya data: https://doi.org/10.5281/zenodo.20539026