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  2. Bayes error rate - Wikipedia

    en.wikipedia.org/wiki/Bayes_error_rate

    Download as PDF; Printable version; In other projects ... This solution is known as the Bayes classifier. ... Naive Bayes classifier; References This page was last ...

  3. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    In the statistics literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. [3] All these names reference the use of Bayes' theorem in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method.

  4. Kernel density estimation - Wikipedia

    en.wikipedia.org/wiki/Kernel_density_estimation

    Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.

  5. Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Bayes_classifier

    In statistical classification, the Bayes classifier is the classifier having the smallest probability of misclassification of all classifiers using the same set of features. [ 1 ] Definition

  6. Bayesian programming - Wikipedia

    en.wikipedia.org/wiki/Bayesian_programming

    Given a description (i.e., ()), a question is obtained by partitioning {,,,} into three sets: the searched variables, the known variables and the free variables. The 3 variables S e a r c h e d {\displaystyle Searched} , K n o w n {\displaystyle Known} and F r e e {\displaystyle Free} are defined as the conjunction of the variables belonging to ...

  7. Minimax estimator - Wikipedia

    en.wikipedia.org/wiki/Minimax_estimator

    For example, the ML estimator from the previous example may be attained as the limit of Bayes estimators with respect to a uniform prior, [,] with increasing support and also with respect to a zero-mean normal prior (,) with increasing variance. So neither the resulting ML estimator is unique minimax nor the least favorable prior is unique.

  8. Bayesian classifier - Wikipedia

    en.wikipedia.org/wiki/Bayesian_classifier

    a Bayes classifier, one that always chooses the class of highest posterior probability in case this posterior distribution is modelled by assuming the observables are independent, it is a naive Bayes classifier

  9. Bayesian experimental design - Wikipedia

    en.wikipedia.org/wiki/Bayesian_experimental_design

    In numerous publications on Bayesian experimental design, it is (often implicitly) assumed that all posterior probabilities will be approximately normal. This allows for the expected utility to be calculated using linear theory, averaging over the space of model parameters. [2]