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  2. 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 ...

  3. Multiclass classification - Wikipedia

    en.wikipedia.org/wiki/Multiclass_classification

    At prediction time, a voting scheme is applied: all K (K − 1) / 2 classifiers are applied to an unseen sample and the class that got the highest number of "+1" predictions gets predicted by the combined classifier. [2]: 339 Like OvR, OvO suffers from ambiguities in that some regions of its input space may receive the same number of votes.

  4. Multilevel model - Wikipedia

    en.wikipedia.org/wiki/Multilevel_model

    Another way to analyze hierarchical data would be through a random-coefficients model. This model assumes that each group has a different regression model—with its own intercept and slope. [5] Because groups are sampled, the model assumes that the intercepts and slopes are also randomly sampled from a population of group intercepts and slopes.

  5. Ensemble learning - Wikipedia

    en.wikipedia.org/wiki/Ensemble_learning

    Bayesian model averaging (BMA) makes predictions by averaging the predictions of models weighted by their posterior probabilities given the data. [22] BMA is known to generally give better answers than a single model, obtained, e.g., via stepwise regression , especially where very different models have nearly identical performance in the ...

  6. Hierarchical Dirichlet process - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_Dirichlet_process

    In statistics and machine learning, the hierarchical Dirichlet process (HDP) is a nonparametric Bayesian approach to clustering grouped data. [ 1 ] [ 2 ] It uses a Dirichlet process for each group of data, with the Dirichlet processes for all groups sharing a base distribution which is itself drawn from a Dirichlet process.

  7. Analytic hierarchy process - Wikipedia

    en.wikipedia.org/wiki/Analytic_hierarchy_process

    Human organizations are often structured as hierarchies, where the hierarchical system is used for assigning responsibilities, exercising leadership, and facilitating communication. Familiar hierarchies of "things" include a desktop computer's tower unit at the "top", with its subordinate monitor, keyboard, and mouse "below."

  8. Mixture of experts - Wikipedia

    en.wikipedia.org/wiki/Mixture_of_experts

    Hierarchical mixtures of experts [7] [8] uses multiple levels of gating in a tree. Each gating is a probability distribution over the next level of gatings, and the ...

  9. Random forest - Wikipedia

    en.wikipedia.org/wiki/Random_forest

    These are models built from a training set {(,)} = that make predictions ^ for new points x' by looking at the "neighborhood" of the point, formalized by a weight function W: ^ = = (, ′). Here, W ( x i , x ′ ) {\displaystyle W(x_{i},x')} is the non-negative weight of the i 'th training point relative to the new point x' in the same tree.