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(Confidence Level = 0.036)
"... − 7 ±4 146 BERGFELD 94B CLE2 e+ e − → D∗0 π + X 41±19±8 190 ANJOS 89C TPS γ N → D0 π + X 0 D1(2420) ± DECAY MODES D ∗ 1 (2420) − modes are charge conjugates of modes below. ..."
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− 7 ±4 146 BERGFELD 94B CLE2 e+ e − → D∗0 π + X 41±19±8 190 ANJOS 89C TPS γ N → D0 π + X 0 D1(2420) ± DECAY MODES D ∗ 1 (2420) − modes are charge conjugates of modes below.
(Confidence Level = 0.036)
"... − 7 ±4 146 BERGFELD 94B CLE2 e+ e − → D∗0 π + X 41±19±8 190 ANJOS 89C TPS γ N → D0 π + X 0 D1(2420) ± DECAY MODES D ∗ 1 (2420) − modes are charge conjugates of modes below. ..."
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− 7 ±4 146 BERGFELD 94B CLE2 e+ e − → D∗0 π + X 41±19±8 190 ANJOS 89C TPS γ N → D0 π + X 0 D1(2420) ± DECAY MODES D ∗ 1 (2420) − modes are charge conjugates of modes below.
High confidence visual recognition of persons by a test of statistical independence
 IEEE TRANS. ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1993
"... A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a sample of the ..."
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Cited by 621 (8 self)
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significant bits comprise a 256byte “iris code. ” Statistical decision theory generates identification decisions from ExclusiveOR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons
Confidence Level Estimator of Cosmological Parameters
, 2012
"... Cosmological Models frequently suggest the existence of physical, quantities, e.g. dark energy, we cannot yet observe and measure directly. Their values are obtained indirectly setting them equal to values and accuracy of the associated model parameters which best fit model and observation. Apparent ..."
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. Apparently results are so accurate that some researchers speak of precision cosmology. The accuracy attributed to these indirect values of the physical quantities however does not include the uncertainty of the model used to get them. We suggest a Confidence Level Estimator to be attached to these indirect
Confidence Level Solutions for Stochastic Programming
 CORE Discussion Papers
, 2000
"... We propose an alternative approach to stochastic programming based on MonteCarlo sampling and stochastic gradient optimization. The procedure is by essence probabilistic and the computed solution is a random variable. The associated objective value is doubly random, since it depends on two outcomes: ..."
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Cited by 39 (1 self)
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We propose an alternative approach to stochastic programming based on MonteCarlo sampling and stochastic gradient optimization. The procedure is by essence probabilistic and the computed solution is a random variable. The associated objective value is doubly random, since it depends on two outcomes: the event in the stochastic program and the randomized algorithm. We propose a solution concept in which the probability that the randomized algorithm produces a solution with an expected objective value departing from the optimal one by more than # is small enough. We derive complexity bounds for this process. We show that by repeating the basic process on independent sample, one can significantly sharpen the complexity bounds. Keywords: Stochastic programming, Stochastic subgradient, Complexity estimate. 1 Introduction The handling general probability distributions in stochastic programming is a delicate issue. Indeed, one cannot, in general, compute exact expectations, and thus one ca...
Uncertain convex programs: Randomized solutions and confidence levels
 MATH. PROGRAM., SER. A (2004)
, 2004
"... Many engineering problems can be cast as optimization problems subject to convex constraints that are parameterized by an uncertainty or ‘instance’ parameter. Two main approaches are generally available to tackle constrained optimization problems in presence of uncertainty: robust optimization and ..."
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Cited by 115 (14 self)
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are enforced up to a prespecified level of probability. Unfortunately however, both approaches lead to computationally intractable problem formulations. In this paper, we consider an alternative ‘randomized ’ or ‘scenario ’ approach for dealing with uncertainty in optimization, based on constraint sampling
ON THE USE OF CONFIDENCE LEVELS IN RISK MANAGEMENT
, 1984
"... A framework for incorporating uncertainty in risk management is developed and applied to two aspects of decision making: meeting standards or safety goals, and costbenefit criteria. The framework isapplied to several case studies including toxic chemicals in water, failure of civil engineering str ..."
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structures and nuclear power plants. The framework proposes that decisions be based on a level of confidence, in addition to comparing best estimate or point values with standards and goals. Int roduct ion At the present ime, there is heightened interest in developing quantitative standards for technologies
Confidence Level Estimation and Analysis Optimization
, 1997
"... This note proposes a method, which can be applied to searches and more in During the last years at LEP a new attitude toward searches has developed which tends to separate the actual search for new phenomena from the derivation of an exclusion limit. In this note we would propose a new method of glo ..."
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Cited by 3 (0 self)
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This note proposes a method, which can be applied to searches and more in During the last years at LEP a new attitude toward searches has developed which tends to separate the actual search for new phenomena from the derivation of an exclusion limit. In this note we would propose a new method of globally optimized analysis which combines the two
Results 1  10
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