Information Collection on a Graph

References

• Barabási A. L., Albert R. Emergence of scaling in random networks. Science (1999) 286(5439):509–512Crossref, Google Scholar
• Bechhofer R. E., Santner T. J., Goldsman D. M.Design and Analysis of Experiments for Statistical Selection, Screening and Multiple Comparisons (1995) (John Wiley & Sons, New York) Google Scholar
• Bernardo J. M., Smith A. F. M.Bayesian Theory (1994) (John Wiley & Sons, New York) Crossref, Google Scholar
• Branke J., Chick S. E., Schmidt C. Selecting a selection procedure. Management Sci. (2007) 53(12):1916–1932Link, Google Scholar
• Chick S., Gans N. Economic analysis of simulation selection options. Management Sci. (2009) 55(3):421–437Link, Google Scholar
• Chick S. E., Frazier P. I., Rosetti M. D., Hill R. R., Johansson B., Dunkin A., Ingalls R. G. The conjunction of the knowledge gradient and the economic approach to simulation selection. Proc. 2009 Winter Simulation Conf. (2009) (IEEE, Austin, TX) 528–539Crossref, Google Scholar
• Chick S. E., Inoue K. New procedures to select the best simulated system using common random numbers. Management Sci. (2001a) 47(8):1133–1149Link, Google Scholar
• Chick S. E., Inoue K. New two-stage and sequential procedures for selecting the best simulated system. Oper. Res. (2001b) 49(5):732–743Link, Google Scholar
• Chick S. E., Branke J., Schmidt C. Sequential sampling to myopically maximize the expected value of information. INFORMS J. Comput. (2010) 22(1):71–80Link, Google Scholar
• Dearden R., Friedman N., Russell S. Bayesian Q-learning. Proc. Fifteenth National Conf. Artificial Intelligence (AAAI-98) (1998) (AAAI Press/MIT Press, Cambridge, MA) Google Scholar
• DeGroot M. H.Optimal Statistical Decisions (1970) (John Wiley & Sons, New York) Google Scholar
• Duff M. O., Barto A. G. Local bandit approximation for optimal learning problems. Advances in Neural Information Processing Systems (1996) 9(MIT Press, Cambridge, MA) 1019–1025Google Scholar
• Erdős P., Renyi A. On random graphs. Publicationes Mathematicae (1959) 6:290–297Crossref, Google Scholar
• Fan Y. Y., Kalaba R. E., Moore J. E. Shortest paths in stochastic networks with correlated link costs. Comput. Math. Appl. (2005) 49(9–10):1549–1564Crossref, Google Scholar
• Frazier P. I., Powell W. B. Consistency of sequential Bayesian sampling policies. SIAM J. Control Optim. (2011) . ForthcomingCrossref, Google Scholar
• Frazier P. I., Powell W. B., Dayanik S. A knowledge gradient policy for sequential information collection. SIAM J. Control Optim. (2008) 47(5):2410–2439Crossref, Google Scholar
• Frazier P. I., Powell W. B., Dayanik S. The knowledge-gradient policy for correlated normal rewards. INFORMS J. Comput. (2009) 21(4):599–613Link, Google Scholar
• Frieze A., Grimmett G. The shortest-path problem for graphs with random arc-lengths. Discrete Appl. Math. (1985) 10(1):57–77Crossref, Google Scholar
• Gelman A. B., Carlin J. B., Stern H. S., Rubin D. B.Bayesian Data Analysis (2004) 2nd ed.(CRC Press, Boca Raton, FL) Google Scholar
• Gilbert E. N. Random graphs. Ann. Math. Statist. (1959) 30(4):1141–1144Crossref, Google Scholar
• Gittins J. C.Multi-Armed Bandit Allocation Indices (1989) (John Wiley & Sons, New York) Google Scholar
• Goldsman D. Ranking and selection in simulation. Proc. 15th Conf. Winter Simulation (1983) 2(IEEE Press, Piscataway, NJ) 387–394Google Scholar
• Gupta S. S., Miescke K. J. Bayesian look ahead one-stage sampling allocations for selection of the best population. J. Statist. Planning Inference (1996) 54(2):229–244Crossref, Google Scholar
• Hoff P. D.A First Course in Bayesian Statistical Methods (2009) (Springer-Verlag, New York) Crossref, Google Scholar
• Inoue K., Chick S. E., Chen C. An empirical evaluation of several methods to select the best system. ACM Trans. Model. Comput. Simulation (TOMACS) (1999) 9(4):381–407Crossref, Google Scholar
• Kim S. H., Nelson B. L., Henderson S. G., Nelson B. L. Selecting the best system. Simulation (2006) 13(North-Holland Publishing Company, Amsterdam) 501–534Handbooks in Operations Research and Management ScienceCrossref, Google Scholar
• Kim S. H., Nelson B. L. Recent advances in ranking and selection. Proc. 39th Conf. Winter Simulation (2007) (IEEE Press, Piscataway, NJ) 162–172Google Scholar
• Kulkarni V. G. Shortest paths in networks with exponentially distributed arc lengths. Networks (1986) 16(3):255–274Crossref, Google Scholar
• Law A. M., Kelton W. D.Simulation Modeling and Analysis (1991) 2nd ed.(McGraw-Hill, Inc., New York) Google Scholar
• Lukacs E. A characterization of the normal distribution. Ann. Math. Statist. (1942) 13(1):91–93Crossref, Google Scholar
• Peer S. K., Sharma D. K. Finding the shortest path in stochastic networks. Comput. Math. Appl. (2007) 53(5):729–740Crossref, Google Scholar
• Ryzhov I. O., Powell W. B. The knowledge gradient algorithm for online subset selection. Proc. 2009 IEEE Sympos. Adaptive Dynamic Programming and Reinforcement Learn. (2009) Nashville, TN:137–144Crossref, Google Scholar
• Ryzhov I. O., Powell W. B., Frazier P. I. The knowledge gradient algorithm for a general class of online learning problems. (2011) . Submitted for publicationGoogle Scholar
• Schmeiser B. Batch size effects in the analysis of simulation output. Oper. Res. (1982) 30(3):556–568Link, Google Scholar
• Snyder T. L., Steele J. M., Ball M., Magnanti T., Monma C. Probabilistic networks and network algorithms. Networks (1995) 7(North-Holland Publishing Company, Amsterdam) 401–424Handbooks in Operations Research and Management ScienceCrossref, Google Scholar
• Watkins C. J. C. H., Dayan P. Q-Learning. Machine Learn. (1992) 8(3):279–292Crossref, Google Scholar