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Estimating Multinomial Probabilities
The American Statistician, 1996Abstract Classical maximum likelihood (ML) as well as the uniformly minimum variance unbiased (UMVU) estimators of multinomial cell probabilities are given by the observed relative frequencies. Bayes estimators corresponding to symmetric Dirichlet prior distribution for p are the inflated observed relative cell frequencies of the type (ni + k) (M + kt)−
S. Kunte, K. S. Upadhya
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Structured estimation, II: Multivariate probability density estimation
IEEE Transactions on Information Theory, 1981We continue the research begun in 1975 on structured estimation. The original work in 1976 by Morgera and Cooper dealt with the Gaussian two-category classification problem when the common covariance matrix is unknown and must be estimated in order to approximate the hyperplane for decisionmaking, which is optimum for the true covariance matrix.
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Theory of Probability & Its Applications, 1960
The problem of estimating an unknown probability in a series of independent trials is dealt -with. The experimental plan differs from the Bernoulli scheme. It is proved that the Clopper-Pearson method is applicable for the construction of interval estimates in an extensive class of :sequential type plans. The point and interval estimators for the Polya
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The problem of estimating an unknown probability in a series of independent trials is dealt -with. The experimental plan differs from the Bernoulli scheme. It is proved that the Clopper-Pearson method is applicable for the construction of interval estimates in an extensive class of :sequential type plans. The point and interval estimators for the Polya
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Hierarchical Probability Estimation
Procedings of the British Machine Vision Conference 1994, 1994Estimating probabilities based on measured numbers of occurrences of events provides a central link from probability theory to real world applications. In an important class of applications the probabilistic events correspond to the digitized outcome of an analog sensor. This paper shows theoretically and experimentally that such events are governed by
Martin Bichsel, Krystyna W. Ohnesorge
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Response probability estimation
Journal of Statistical Planning and Inference, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Estimating Failure Probabilities
2019System failure describes an undesired configuration of an engineering device, possibly leading to the destruction of material or a significant loss of performance and a consequent loss of yield. For systems subject to uncertainties, failure probabilities express the probability of this undesired configuration to take place.
ter Maten, E. Jan W. +11 more
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State Estimation with Probability Constraints
Proceedings of the 44th IEEE Conference on Decision and Control, 2006This paper considers a state estimation problem for a discrete-time linear system driven by a Gaussian random process. The second order statistics of the input process and state initial condition are uncertain. However, the probability that the state and input satisfy linear constraints during the estimation interval is known.
Mario Rotea, Carlos Lana
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Probability Density Estimation
1995Abstract Density estimation can also be applied to unlabelled data (that is data without any class labels) where it has a number of applications. In the context of neural networks it can be applied to the distribution of data in the input space as part of the training process for radial basis function networks (Section 5.9), and to ...
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Journal of the Hydraulics Division, 1982
A procedure is developed for estimating design floods in situations where data are insufficient for a conventional frequency analysis. It involves the use of a compound distribution which is a weighted combination of individual two parameter distributions. Initial values are assigned to the parameters of the component distributions and their weights on
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A procedure is developed for estimating design floods in situations where data are insufficient for a conventional frequency analysis. It involves the use of a compound distribution which is a weighted combination of individual two parameter distributions. Initial values are assigned to the parameters of the component distributions and their weights on
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Probability Density Estimation
2002For observational data, (1.5) of Example 1.2 defines penalized likelihood density estimation. Of interest are the selection of smoothing parameters, the computation of the estimates, and the asymptotic behavior of the estimates. Variants of (1.5) are also called for to accommodate samples subject to selection bias and samples from conditional ...
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