Kianzilishi cha Kutokugeuka-nyuma (NUTS)
Kianzilishi cha Kutokugeuka-nyuma (NUTS) ni algoriti ya kiotomatiki ya mnyororo wa Markov Monte Carlo iliyoanzishwa na Hoffman na Gelman (2014) ambayo inapanua Hamiltonian Monte Carlo (HMC) kwa kuamua kiotomatiki idadi bora ya hatua za kuruka-fuko, na kuondoa kigezo nyeti zaidi cha urekebishaji wa mikono. NUTS ndicho kianzilishi chaguo-msingi katika Stan na PyMC na kimefanya uchanganuzi wa Bayesian kwa kiwango kikubwa na chenye vipimo vingi kupatikana kwa vitendo bila kuwahitaji watumiaji kuweka urefu wa njia kwa mkono.
Soma mbinu kamili
Ingia kwa akaunti ya bure ili kusoma sehemu hii.
Method map
The neighbourhood of related methods — select a node to explore.
Vyanzo
- Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(47), 1593–1623. link ↗
- Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. L. Jones, & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113–162). CRC Press. DOI: 10.1201/b10905-6 ↗
- 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: 978-1-4398-4095-5
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 3). No-U-Turn Sampler (NUTS). ScholarGate. https://scholargate.app/sw/bayesian/no-u-turn-sampler
Which method?
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.
- Usajili wa BayesianMbinu za Bayes↔ compare
- Hamiltonian Monte CarloMbinu za Bayes↔ compare
- Markov Chain Monte Carlo (MCMC)Mbinu za Bayes↔ compare
- Utoaji wa KigezoMbinu za Bayes↔ compare
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