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  2. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    Abstractly, naive Bayes is a conditional probability model: it assigns probabilities (, …,) for each of the K possible outcomes or classes given a problem instance to be classified, represented by a vector = (, …,) encoding some n features (independent variables).

  3. Linear classifier - Wikipedia

    en.wikipedia.org/wiki/Linear_classifier

    Linear Discriminant Analysis (LDA)—assumes Gaussian conditional density models; Naive Bayes classifier with multinomial or multivariate Bernoulli event models. The second set of methods includes discriminative models, which attempt to maximize the quality of the output on a training set.

  4. Generative model - Wikipedia

    en.wikipedia.org/wiki/Generative_model

    Types of generative models are: Gaussian mixture model (and other types of mixture model) Hidden Markov model; Probabilistic context-free grammar; Bayesian network (e.g. Naive bayes, Autoregressive model) Averaged one-dependence estimators; Latent Dirichlet allocation; Boltzmann machine (e.g. Restricted Boltzmann machine, Deep belief network)

  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 inference - Wikipedia

    en.wikipedia.org/wiki/Bayesian_inference

    Bayesian inference (/ ˈ b eɪ z i ə n / BAY-zee-ən or / ˈ b eɪ ʒ ən / BAY-zhən) [1] is a method of statistical inference in which Bayes' theorem is used to calculate a probability of a hypothesis, given prior evidence, and update it as more information becomes available.

  7. Predictive Model Markup Language - Wikipedia

    en.wikipedia.org/wiki/Predictive_Model_Markup...

    Enhancements to Scorecard and Naive Bayes model elements; PMML 4.3 was released on August 23, 2016. [13] [14] New features include: New Model Types: Gaussian Process; Bayesian Network; New built-in functions; Usage clarifications; Documentation improvements; Version 4.4 was released in November 2019. [15] [16]

  8. Neural network Gaussian process - Wikipedia

    en.wikipedia.org/wiki/Neural_network_Gaussian...

    A Neural Network Gaussian Process (NNGP) is a Gaussian process (GP) obtained as the limit of a certain type of sequence of neural networks. Specifically, a wide variety of network architectures converges to a GP in the infinitely wide limit , in the sense of distribution .

  9. Bayesian linear regression - Wikipedia

    en.wikipedia.org/wiki/Bayesian_linear_regression

    Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often ...