TY - GEN
T1 - The knowledge-gradient stopping rule for ranking and selection
AU - Frazier, Peter
AU - Powell, Warren B.
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - We consider the ranking and selection of normal means in a fully sequential Bayesian context. By considering the sampling and stopping problems jointly rather than separately, we derive a new composite stopping/sampling rule. The sampling component of the derived composite rule is the same as the previously introduced LL1 sampling rule, but the stopping rule is new. This new stopping rule significantly improves the performance of LL1 as compared to its performance under the best other generally known adaptive stopping rule, EOC Bonf, outperforming it in every case tested.
AB - We consider the ranking and selection of normal means in a fully sequential Bayesian context. By considering the sampling and stopping problems jointly rather than separately, we derive a new composite stopping/sampling rule. The sampling component of the derived composite rule is the same as the previously introduced LL1 sampling rule, but the stopping rule is new. This new stopping rule significantly improves the performance of LL1 as compared to its performance under the best other generally known adaptive stopping rule, EOC Bonf, outperforming it in every case tested.
UR - http://www.scopus.com/inward/record.url?scp=60749102402&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=60749102402&partnerID=8YFLogxK
U2 - 10.1109/WSC.2008.4736082
DO - 10.1109/WSC.2008.4736082
M3 - Conference contribution
AN - SCOPUS:60749102402
SN - 9781424427086
T3 - Proceedings - Winter Simulation Conference
SP - 305
EP - 312
BT - Proceedings of the 2008 Winter Simulation Conference, WSC 2008
T2 - 2008 Winter Simulation Conference, WSC 2008
Y2 - 7 December 2008 through 10 December 2008
ER -