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  2. 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

  3. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    Example of a naive Bayes classifier depicted as a Bayesian Network. In statistics, naive Bayes classifiers are a family of linear "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class. The strength (naivety) of this assumption is what gives the classifier its name.

  4. Bayesian inference - Wikipedia

    en.wikipedia.org/wiki/Bayesian_inference

    ] Bayesian inference has gained popularity among the phylogenetics community for these reasons; a number of applications allow many demographic and evolutionary parameters to be estimated simultaneously. As applied to statistical classification, Bayesian inference has been used to develop algorithms for identifying e-mail spam.

  5. Bayesian statistics - Wikipedia

    en.wikipedia.org/wiki/Bayesian_statistics

    Bayes' theorem describes the conditional probability of an event based on data as well as prior information or beliefs about the event or conditions related to the event. [3] [4] For example, in Bayesian inference, Bayes' theorem can be used to estimate the parameters of a probability distribution or statistical model. Since Bayesian statistics ...

  6. Naive Bayes spam filtering - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_spam_filtering

    A Bayesian spam filter will eventually assign a higher probability based on the user's specific patterns. The legitimate e-mails a user receives will tend to be different. For example, in a corporate environment, the company name and the names of clients or customers will be mentioned often.

  7. Bayesian probability - Wikipedia

    en.wikipedia.org/wiki/Bayesian_probability

    Bayesian probability (/ ˈ b eɪ z i ə n / BAY-zee-ən or / ˈ b eɪ ʒ ən / BAY-zhən) [1] is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation [2] representing a state of knowledge [3] or as quantification of a personal belief.

  8. Classification rule - Wikipedia

    en.wikipedia.org/wiki/Classification_rule

    We can use Bayes' theorem to determine the probability that the positive result is in fact a true positive using the example from above: If a tested patient has the disease, the test returns a positive result 99% of the time, or with a probability of 0.99.

  9. Bayesian hierarchical modeling - Wikipedia

    en.wikipedia.org/wiki/Bayesian_hierarchical_modeling

    Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method. [1] The sub-models combine to form the hierarchical model, and Bayes' theorem is used to integrate them with the observed data and account for all the ...