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Interval Estimation Naïve Bayes [PDF]
, 2003 Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier called naïve Bayes is competitive with state of the art classifiers. This simple approach stands from assumptions of conditional independence among features given the class.
Robles Forcada, Víctor +4 more
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An improved Bayes empirical Bayes estimator [PDF]
International Journal of Mathematics and Mathematical Sciences, 2003 Consider an experiment yielding an observable random quantity X whose distribution Fθ depends on a parameter θ with θ being distributed according to some distribution G0. We study the Bayesian estimation problem of θ under squared error loss function based on X, as well as some additional data available from other similar experiments according to an ...
R. J. Karunamuni, N. G. N. Prasad
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Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study [PDF]
, 2001 Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-variable objects. We discuss in detail wavelet methods in nonparametric regression, where the data are modelled as observations of a signal contaminated ...
Theofanis Sapatinas +5 more
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Bayesian Inference and Prediction for Normal Distribution Based on Records
Statistica, 2018 Based on record data, the estimation and prediction problems for normal distribution have been investigated by several authors in the frequentist set up. However, these problems have not been discussed in the literature in the Bayesian context.
Akbar Asgharzadeh +3 more
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The Annals of Mathematical Statistics, 1967 Throughout this paper we are concerned with the problem of estimating a real parameter when the loss function is such that the Bayes estimate exists, is unique, and satisfies a simple Equation, (1.5). If the estimate is unbiased (in the general sense of Lehmann [3]) we show under weak conditions that it must satisfy another Equation, (1.14).
Bickel, Peter J., Blackwell, David
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Empirical Bayes and Full Bayes for Signal Estimation
CoRR, 2014 We consider signals that follow a parametric distribution where the parameter values are unknown. To estimate such signals from noisy measurements in scalar channels, we study the empirical performance of an empirical Bayes (EB) approach and a full Bayes (FB) approach.
Yanting Ma +3 more
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Axioms, 2023 This study addresses the issue of estimating the shape parameter of the inverted exponentiated Rayleigh distribution, along with the assessment of reliability and failure rate, by utilizing Type-I progressive hybrid censored data.
Atef F. Hashem +2 more
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Nonparametric Bayes-risk estimation [PDF]
IEEE Transactions on Information Theory, 1971 Two nonparametric methods to estimate the Bayes risk using classified sample sets are described and compared. The first method uses the nearest neighbor error rate as an estimate to bound the Bayes risk. The second method estimates the Bayes decision regions by applying Parzen probability-density function estimates and counts errors made using these ...
Stanley C. Fralick, Richard W. Scott
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Estimation of Dynamic Cumulative Past Entropy for Power Function Distribution
Statistica, 2019 In this paper, we proposed MLE and Bayes estimators of parameters and DCPE for the two parameter power function distribution. Bayes estimators under different loss functions are obtained using Lindley approximation method and important sampling ...
Enchakudiyil Ibrahim Abdul-Sathar +1 more
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مجلة بغداد للعلوم, 2013 In this paper, Bayes estimators of the parameter of Maxwell distribution have been derived along with maximum likelihood estimator. The non-informative priors; Jeffreys and the extension of Jeffreys prior information has been considered under two ...
Baghdad Science Journal
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