A survey of algorithmic methods for partially observed Markov decision processes
Annals of Operations Research
Searching for important factors: sequential bifurcation under uncertainty
Proceedings of the 29th conference on Winter simulation
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
A Knowledge-Gradient Policy for Sequential Information Collection
SIAM Journal on Control and Optimization
The knowledge-gradient stopping rule for ranking and selection
Proceedings of the 40th Conference on Winter Simulation
Economic Analysis of Simulation Selection Problems
Management Science
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
Sequential Sampling to Myopically Maximize the Expected Value of Information
INFORMS Journal on Computing
INFORMS Journal on Computing
Sequential Sampling with Economics of Selection Procedures
Management Science
A Bayesian approach to stochastic root finding
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
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Sequential screening is the problem of allocating simulation effort to identify those input factors that have an important effect on a simulation's output. In this problem, sophisticated algorithms can be substantially more efficient than simulating one factor at a time. We consider this problem in a Bayesian framework, in which each factor is important independently and with a known probability. We use dynamic programming to compute the Bayes-optimal method for splitting factors among groups within a sequential bifurcation procedure (Bettonvil & Kleijnen 1997). We assume importance can be tested without error. Numerical experiments suggest that existing group-splitting rules are optimal, or close to optimal, when factors have homogeneous importance probability, but that substantial gains are possible when factors have heterogeneous probability of importance.