Bayes


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Bayes

(bāyz),
Thomas, British mathematician, 1702-1761. See: Bayes theorem.
References in periodicals archive ?
A number of approaches have been proposed including attribute weighting, feature selection, and so forth to improve the performance of Naive Bayes algorithm [9].
The Bayes estimate of the unconditional variance using the median parameter estimates is smaller than that of the ML parameter estimates.
Naive Bayes classifiers are linear classifiers that are known for being simple yet very efficient.
The Bayes Decision Method is the way of using the prior information and the sample information of the parameters to make decisions.
WEKA, a machine learning software, described in section 4, is used to classify selected datasets using Naive Bayes, Bagging, Boosting, and Stacking.
In our study priors are quite flexible but in general set up it is not possible to obtain the Bayes estimates in explicit form.
Thinking like Bayes - with or without numbers - you will do this less often.
This approach has been used by several authors like [3, 6] to obtain the approximate Bayes estimators.
For this study, we applied a naive Bayes classifier to a robust public health dataset, with greedy feature selection, with the objective of efficiently identifying that the n attributes which best predict a selected target attribute, without searching the input space exhaustively.
The objective of the analysis that follows is to show how the statistical Bayes formula (theorem) to update beliefs (5-10) can be used to interpret the TD (PoF) information and to determine if the device (system) of interest is still sound (healthy) or has become faulty, and to use this information to identify a faulty device, if any.
Bayes (political science, California State University-Northridge) assembles international contributors to explore perspectives on gender and politics around the world.