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Selecting The Best System: Theory And Methods
, 2003
"... This paper provides an advanced tutorial on the construction of rankingandselection procedures for selecting the best simulated system. We emphasize procedures that provide a guaranteed probability of correct selection, and the key theoretical results that are used to derive them. ..."
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Cited by 12 (1 self)
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This paper provides an advanced tutorial on the construction of rankingandselection procedures for selecting the best simulated system. We emphasize procedures that provide a guaranteed probability of correct selection, and the key theoretical results that are used to derive them.
Selecting The Best System: A DecisionTheoretic Approach
 In Proc. 1997 Winter Simulation Conference
, 1997
"... The problem of selecting the best system from a finite set of alternatives is considered from a Bayesian decisiontheoretic perspective. The framework presented is quite general, and permits selection from two or more systems, with replications that use either independent or common random numbers, w ..."
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Cited by 10 (2 self)
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The problem of selecting the best system from a finite set of alternatives is considered from a Bayesian decisiontheoretic perspective. The framework presented is quite general, and permits selection from two or more systems, with replications that use either independent or common random numbers, with unknown means and covariances for the output, and permits Gaussian or nonGaussian simulation output. For the case of unknown mean and variance with common random numbers, the framework provides a probability of correct selection that does not suffer from problems associated with the Bonferroni inequality. We indicate some criteria for which the Bayesian approach and other approaches are in general agreement, or disagreement. The probability of correct selection can be calculated either by quadrature or by Monte Carlo simulation from the posterior distribution of the parameters of the statistical distribution of the simulation output. We also comment on expectedvalue decisionmaking ver...
Deriving Stopping Rules for the Probabilistic Hough Transform by Sequential Analysis
 Computer Vision and Image Understanding
, 1996
"... It is known that Hough Transform computation can be significantly accelerated by polling instead of voting. A small part of the data set is selected at random and used as input to the algorithm. The performance of these Probabilistic Hough Transforms depends on the poll size. Most Probabilistic H ..."
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Cited by 9 (1 self)
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It is known that Hough Transform computation can be significantly accelerated by polling instead of voting. A small part of the data set is selected at random and used as input to the algorithm. The performance of these Probabilistic Hough Transforms depends on the poll size. Most Probabilistic Hough algorithms use a fixed poll size, which is far from optimal since conservative design requires the fixed poll size to be much larger than necessary in average conditions. It has recently been experimentally demonstrated that adaptive termination of voting can lead to improved performance in terms of the error rate versus average poll size tradeoff. However, the lack of a solid theoretical foundation made general performance evaluation and optimal design of adaptive stopping rules nearly impossible.
MonteCarlo Sampling for NPHard Maximization Problems in the Framework of Weighted Parsing
 Natural Language Processing  NLP 2000, number 1835 in Lecture Notes in Artificial Intelligence
, 2000
"... The purpose of this paper is (1) to provide a theoretical justification for the use of MonteCarlo sampling for approximate resolution of NPhard maximization problems in the framework of weighted parsing, and (2) to show how such sampling techniques can be e#ciently implemented with an explicit ..."
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Cited by 3 (3 self)
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The purpose of this paper is (1) to provide a theoretical justification for the use of MonteCarlo sampling for approximate resolution of NPhard maximization problems in the framework of weighted parsing, and (2) to show how such sampling techniques can be e#ciently implemented with an explicit control of the error probability. We provide an algorithm to compute the local sampling probability distribution that guarantee that the global sampling probability indeed corresponds to the aimed theoretical score. The proposed sampling strategy significantly di#ers from existing methods, showing by the same way the bias induced by these methods.
Sequential Update of ADtrees
"... Ingcreasingly, datamining algorithms must deal with databases that continuously grow over time. These algorithms must avoid repeatedly scanning their databases. When database attributes are symbolic, ADtrees have already shown to be efficient structures to store sufficient statistics in main memory ..."
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Cited by 1 (0 self)
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Ingcreasingly, datamining algorithms must deal with databases that continuously grow over time. These algorithms must avoid repeatedly scanning their databases. When database attributes are symbolic, ADtrees have already shown to be efficient structures to store sufficient statistics in main memory and to accelerate the mining process in batch environments. Here we present an efficient method to sequentially update ADtrees that is suitable for incremental environments. 1.
Estimating the Probability that a Simulated System Will be the Best
 Naval Research Logistics
, 2002
"... Consider a stochastic simulation experiment that generates v independent vector replications consisting of an observation from each of k systems. Typical system comparisons are based on mean (longrun) performance. However, the probability that a system will actually be the best is sometimes more re ..."
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Consider a stochastic simulation experiment that generates v independent vector replications consisting of an observation from each of k systems. Typical system comparisons are based on mean (longrun) performance. However, the probability that a system will actually be the best is sometimes more relevant, and can provide a very different perspective than the systems' means. Empirically, we select one system as the best performer (i.e., it wins) on each replication. Each system has an unknown constant probability of winning on any replication and the numbers of wins for the individual systems follow a multinomial distribution. Procedures exist for finding the system with the largest probability of being the best. This paper addresses the companion problem of estimating the probability that each system will be the best. The maximum likelihood estimators (MLEs) of the multinomial cell probabilities for a set of v vector replications across k systems are well known. We use these same v ve...
A GENERAL FRAMEWORK FOR THE ASYMPTOTIC VALIDITY OF TWOSTAGE PROCEDURES FOR SELECTION AND MULTIPLE COMPARISONS WITH CONSISTENT VARIANCE ESTIMATORS
"... We consider twostage procedures for selection and multiple comparisons, where the variance parameter is estimated consistently. We examine conditions under which the procedures are asymptotically valid in a general framework. Our proofs of asymptotic validity require that the estimators at the end ..."
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We consider twostage procedures for selection and multiple comparisons, where the variance parameter is estimated consistently. We examine conditions under which the procedures are asymptotically valid in a general framework. Our proofs of asymptotic validity require that the estimators at the end of the second stage are asymptotically normal, so we require a randomtimechange central limit theorem. We explain how the assumptions hold for comparing means in transient simulations, steadystate simulations and quantile estimation, but the assumptions are also valid for many other problems arising in simulation studies. 1
TREATMENTS IN CLINICAL TRIALS Approved by:
"... iii ACKNOWLEDGEMENTS I would like to thank a number of individuals for their support and assistance during my study at Georgia Tech. Foremost, I want to express my special gratitude to my advisor, Dr. Anthony Hayter, for his inspiration, guidance, and encouragement. He helps me a lot through the who ..."
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iii ACKNOWLEDGEMENTS I would like to thank a number of individuals for their support and assistance during my study at Georgia Tech. Foremost, I want to express my special gratitude to my advisor, Dr. Anthony Hayter, for his inspiration, guidance, and encouragement. He helps me a lot through the whole Ph.D. study. I am grateful to Dr. Alexander Shapiro for his insightful instruction. He is a gifted teacher and researcher. I thank Dr. Yajun Mei for helping me with building confidence. The discussion with him is always fruitful and inspires me with new research ideas. I appreciate Dr. Brani Vidakovic and Dr. Nicoleta Serban for their valuable comments on both teaching and research. Their intelligent and precise suggestions make my thesis more complete. I want to extend my warm thanks to my friends here and in Taiwan. Their company makes my life delightful. Last but not least, I would like to show my profound appreciation to my parents, my brother, and Weiyu. Their selfless support
Chapter 17: Selecting the Best System
"... We describe the basic principles of ranking and selection, a collection of experimentdesign techniques for comparing “populations ” with the goal of finding the best among them. We then describe the challenges and opportunities encountered in adapting rankingandselection techniques to stochastic s ..."
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We describe the basic principles of ranking and selection, a collection of experimentdesign techniques for comparing “populations ” with the goal of finding the best among them. We then describe the challenges and opportunities encountered in adapting rankingandselection techniques to stochastic simulation problems, along with key theorems, results and analysis tools that have proven useful in extending them to this setting. Some specific procedures are presented along with a numerical illustration. 1