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Bayesian methodsBayesian / computational

Uigaji wa Monte Carlo kwa Data Zisizokamilika

Uigaji wa Monte Carlo kwa data zisizokamilika unachanganya uigaji wa stochastic — kuchora thamani nasibu kutoka kwenye usambazaji wa uwezekano — na mikakati ya kimsingi ya data zisizokamilika kama vile ujazaji-nyingi (multiple imputation). Badala ya kutupa rekodi zisizokamilika au kubadilisha thamani moja ya kujaza, njia hii huzalisha seti nyingi za data kamili zilizoigizwa, huendesha uchambuzi lengwa kwa kila moja, na kuunganisha matokeo ili kutoa makadirio yanayoakisi kwa uaminifu kutokuwa na uhakika kwa sampuli na kutokuwa na uhakika kutokana na kukosekana kwa data.

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Vyanzo

  1. Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860
  2. van Buuren, S. (2018). Flexible Imputation of Missing Data (2nd ed.). CRC Press / Chapman & Hall. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Monte Carlo Simulation with Missing Data Handling. ScholarGate. https://scholargate.app/sw/bayesian/monte-carlo-simulation-with-missing-data

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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.

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Imerejelewa na

ScholarGateMonte Carlo Simulation with Missing Data (Monte Carlo Simulation with Missing Data Handling). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/bayesian/monte-carlo-simulation-with-missing-data · Seti ya data: https://doi.org/10.5281/zenodo.20539026