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Ulinganifu wa wastani wa modeli za kibayazilishi zenye viwango vingi

Ulinganifu wa wastani wa modeli za kibayazilishi zenye viwango vingi (ML-BMA) huongeza ulinganifu wa wastani wa modeli za kibayazilishi za kawaida kwa data zilizopangwa au zenye muundo wa ngazi nyingi. Badala ya kujitolea kwa vipimo moja tu vya modeli zenye viwango vingi, huhesabu wastani wenye uzito wa utabiri na makadirio ya vigezo katika seti ya modeli zinazoshindana zenye viwango vingi, ikipima kila modeli kwa uwezekano wake wa baada ya ushahidi kulingana na data. Matokeo huzingatia kwa wakati mmoja kutokuwa na uhakika katika muundo wa upambanuo, athari zilizowekwa, athari za nasibu, na uteuzi wa vigezo.

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Vyanzo

  1. Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-401. link
  2. 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: 978-1439840955

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Multilevel Bayesian Model Averaging. ScholarGate. https://scholargate.app/sw/bayesian/multilevel-bayesian-model-averaging

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ScholarGateMultilevel Bayesian Model Averaging (Multilevel Bayesian Model Averaging). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/bayesian/multilevel-bayesian-model-averaging · Seti ya data: https://doi.org/10.5281/zenodo.20539026