Gaussian approximation theorems for urn models and their applications
By Z. Bai, Feifang Hu, Li-Xin Zhang. 2002
52 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Mathematical Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/2abd04744bd5f660c00fcc78067fc94ac497b696
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In Artificial Intelligence
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Citations in the corpus
- Asymptotic theorems of sequential estimation-adjusted urn models (2006, unknown): “…v ) Hm+1 + n∑ m=1 v(Hm −H) +Y0 =Mn + n−1∑ m=0 ( Ym |Ym| − v ) H+ n∑ m=1 v(Hm −H) + n−1∑ m=0 ( Ym |Ym| − v ) (Hm+1 −H) +Y0 (5.5) =Mn + n−1∑ m=0 ( Ym |Ym| − v ) H+ n∑ m=1 v(Hm −H) + n−1∑ m=0 ( Ym |Ym| − v ) (Hm+1 −H) +Y0 =Mn + n−1∑ m=0 Ym −mv m+1 H+Rn, where Rn = n∑ m=1 v(Hm −H) + n−1∑ m=0 ( Ym |Ym|…”
- A proof of a conjecture of Buck, Chan, and Robbins on the expected value of the minimum assignment (2005, result): “) See also similar results in [22], [4], [5].”
- Asymptotics in response-adaptive designs generated by a two-color, randomly reinforced urn (2009, background): “Among more recent literature on urn models and adaptive designs, there are, for instance, [3, 4, 16, 30].”