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  2. EM algorithm and GMM model - Wikipedia

    en.wikipedia.org/wiki/EM_Algorithm_And_GMM_Model

    The EM algorithm consists of two steps: the E-step and the M-step. Firstly, the model parameters and the () can be randomly initialized. In the E-step, the algorithm tries to guess the value of () based on the parameters, while in the M-step, the algorithm updates the value of the model parameters based on the guess of () of the E-step.

  3. Mixture model - Wikipedia

    en.wikipedia.org/wiki/Mixture_model

    A typical finite-dimensional mixture model is a hierarchical model consisting of the following components: . N random variables that are observed, each distributed according to a mixture of K components, with the components belonging to the same parametric family of distributions (e.g., all normal, all Zipfian, etc.) but with different parameters

  4. Expectation–maximization algorithm - Wikipedia

    en.wikipedia.org/wiki/Expectation–maximization...

    It can be used, for example, to estimate a mixture of gaussians, or to solve the multiple linear regression problem. [2] EM clustering of Old Faithful eruption data. The random initial model (which, due to the different scales of the axes, appears to be two very flat and wide ellipses) is fit to the observed data.

  5. Model-based clustering - Wikipedia

    en.wikipedia.org/wiki/Model-based_clustering

    Model-based clustering [1] bases this on a statistical model for the data, usually a mixture model. This has several advantages, including a principled statistical basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to identify outliers that do ...

  6. Generative model - Wikipedia

    en.wikipedia.org/wiki/Generative_model

    a generative model is a model of the conditional probability of the observable X, given a target y, symbolically, (=) [2] a discriminative model is a model of the conditional probability of the target Y , given an observation x , symbolically, P ( Y ∣ X = x ) {\displaystyle P(Y\mid X=x)} [ 3 ]

  7. Gaussian mixture model - Wikipedia

    en.wikipedia.org/?title=Gaussian_mixture_model&...

    This page was last edited on 12 October 2018, at 17:51 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.

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    Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!

  9. Mixture distribution - Wikipedia

    en.wikipedia.org/wiki/Mixture_distribution

    In probability and statistics, a mixture distribution is the probability distribution of a random variable that is derived from a collection of other random variables as follows: first, a random variable is selected by chance from the collection according to given probabilities of selection, and then the value of the selected random variable is realized.