STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Sampling algorithms: lower bounds and applications
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Journal of Artificial Intelligence Research
Ranking continuous probabilistic datasets
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UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Optimal Monte Carlo estimation of belief network inference
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Bounded rational search for on-the-fly model checking of LTL properties
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Robust network supercomputing with malicious processes
DISC'06 Proceedings of the 20th international conference on Distributed Computing
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Efficient Approximation and Online Algorithms
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A typical approach to estimate an unknown quantity /spl mu/ is to design an experiment that produces a random variable Z distributed in [O,1] with E[Z]=/spl mu/, run this experiment independently a number of times and use the average of the outcomes as the estimate. In this paper, we consider the case when no a priori information about Z is known except that is distributed in [0,1]. We describe an approximation algorithm AA which, given /spl epsiv/ and /spl delta/, when running independent experiments with respect to any Z, produces an estimate that is within a factor 1+/spl epsiv/ of /spl mu/ with probability at least 1-/spl delta/. We prove that the expected number of experiments ran by AA (which depends on Z) is optimal to within a constant factor for every Z.