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CONFIDENCE INTERVALS FOR AVERAGE SUCCESS PROBABILITIES∗ BY
"... Abstract. We provide Buehleroptimal onesided and valid twosided confidence intervals for the average success probability of a possibly inhomogeneous fixed length Bernoulli chain, based on the number of observed successes. Contrary to some claims in the literature, the onesided Clopper– Pearson ..."
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Abstract. We provide Buehleroptimal onesided and valid twosided confidence intervals for the average success probability of a possibly inhomogeneous fixed length Bernoulli chain, based on the number of observed successes. Contrary to some claims in the literature, the onesided Clopper– Pearson
Estimation of probabilities from sparse data for the language model component of a speech recognizer
 IEEE Transactions on Acoustics, Speech and Signal Processing
, 1987
"... AbstractThe description of a novel type of rngram language model is given. The model offers, via a nonlinear recursive procedure, a computation and space efficient solution to the problem of estimating probabilities from sparse data. This solution compares favorably to other proposed methods. Wh ..."
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Cited by 799 (2 self)
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. While the method has been developed for and successfully implemented in the IBM Real Time Speech Recognizers, its generality makes it applicable in other areas where the problem of estimating probabilities from sparse data arises. Sparseness of data is an inherent property of any real text
The illusion of Control
 Journal of Personality and Social Psychology
, 1975
"... A series of studies was conducted to elucidate a phenomenon here referred to as the "illusion of control. " An illusion of control was denned as an expectancy of a personal success probability inappropriately higher than the objective probability would warrant. It was predicted that fact ..."
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Cited by 607 (1 self)
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A series of studies was conducted to elucidate a phenomenon here referred to as the "illusion of control. " An illusion of control was denned as an expectancy of a personal success probability inappropriately higher than the objective probability would warrant. It was predicted
Success Probability Assessment Based on Information Entropy
, 2009
"... The Bayesian method is superior to the classical statistical method on condition of small sample test. However, its evaluation results are not so good if subjective prior information is intervened. The success probability assessment about the success or failure tests of weapon products focussed in t ..."
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The Bayesian method is superior to the classical statistical method on condition of small sample test. However, its evaluation results are not so good if subjective prior information is intervened. The success probability assessment about the success or failure tests of weapon products focussed
On Field Size and Success Probability in Network Coding
"... Abstract. Using tools from algebraic geometry and Gröbner basis theory we solve two problems in network coding. First we present a method to determine the smallest field size for which linear network coding is feasible. Second we derive improved estimates on the success probability of random linear ..."
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Abstract. Using tools from algebraic geometry and Gröbner basis theory we solve two problems in network coding. First we present a method to determine the smallest field size for which linear network coding is feasible. Second we derive improved estimates on the success probability of random linear
Generalized Hypothesis Testing and Maximizing the Success Probability
 in Financial Markets, Proceedings of the International Conference on Business Intelligence and Financial Engineering (ICBIFE
, 2011
"... Abstract. We study the generalized composite pure and randomized hypothesis testing problems. In addition to characterizing the optimal tests, we examine the conditions under which these two hypothesis testing problems are equivalent, and provide counterexamples when they are not. This analysis is ..."
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Cited by 1 (1 self)
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is useful for portfolio optimization to maximize some success probability given a fixed initial capital. The corresponding dual is related to a pure hypothesis testing problem which may or may not coincide with the randomized hypothesis testing problem. Our framework is applicable to both complete
On The Expected Runtime And The Success Probability Of Evolutionary Algorithms
, 2000
"... Evolutionary algorithms are randomized search heuristics whose general variants have been successfully applied in black box optimization. In this scenario the function f to be optimized is not known in advance and knowledge on f can be obtained only by sampling search points a revealing the val ..."
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Cited by 6 (1 self)
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the value of f(a). In order to analyze the behavior of different variants of evolutionary algorithms on certain functions f , the expected runtime until some optimal search point is sampled and the success probability, i.e., the probability that an optimal search point is among the first sampled points
Maximum entropy markov models for information extraction and segmentation
, 2000
"... Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many textrelated tasks, such as partofspeech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled as multinomial ..."
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Cited by 561 (18 self)
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Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many textrelated tasks, such as partofspeech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled
The Vocabulary Problem in HumanSystem Communication
 COMMUNICATIONS OF THE ACM
, 1987
"... In almost all computer applications, users must enter correct words for the desired objects or actions. For success without extensive training, or in firsttries for new targets, the system must recognize terms that will be chosen spontaneously. We studied spontaneous word choice for objects in five ..."
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Cited by 562 (8 self)
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in five applicationrelated domains, and found the variability to be surprisingly large. In every case two people favored the same term with probability <0.20. Simulations show how this fundamental property of language limits the success of various design methodologies for vocabularydriven interaction
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