Bayesian Algorithms

In Bayesian analysis, the statistical inferences for datasets are updated each time additional data is included. Algorithms under Bayesian classification are based on Bayes’ theorem. Bayesian classifiers are the statistical classifiers.

  • Naive Bayes: This is a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of a feature.
  • Gaussian Naive Bayes: Here, continuous values associated with each feature are assumed to be distributed according to a Gaussian distribution. A Gaussian distribution is also called Normal distribution.
  • Multinomial Naive Bayes: This is mostly used for document classification problem, i.e., whether a document belongs to the category of sports, politics, technology, etc. The features/predictors used by the classifier are the frequency of the words present in the document.
  • Averaged One-Dependence Estimators (AODE): It’s a fast and accurate algorithm based on the average of probabilistic estimates.
  • Bayesian Belief Network (BBN): It’s a graphical model that represents the probabilistic relationships among a set of variables.
  • Bayesian Network (BN): It’s a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph.

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