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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. Expectation–maximization algorithm - Wikipedia

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

    For example, one of the solutions that may be found by EM in a mixture model involves setting one of the components to have zero variance and the mean parameter for the same component to be equal to one of the data points. The convergence of expectation-maximization (EM)-based algorithms typically requires continuity of the likelihood function ...

  4. File:Parameter estimation process infinite Gaussian mixture ...

    en.wikipedia.org/wiki/File:Parameter_estimation...

    Histograms for one-dimensional datapoints belonging to clusters detected by an infinite Gaussian mixture model. During the parameter estimation based on Gibbs sampling , new clusters are created and grow on the data. The legend shows the cluster colours and the number of datapoints assigned to each cluster.

  5. Multifidelity simulation - Wikipedia

    en.wikipedia.org/wiki/Multifidelity_simulation

    A more general class of regression-based multi-fidelity methods are Bayesian approaches, e.g. Bayesian linear regression, [3] Gaussian mixture models, [10] [11] Gaussian processes, [12] auto-regressive Gaussian processes, [2] or Bayesian polynomial chaos expansions.

  6. 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

  7. Subspace Gaussian mixture model - Wikipedia

    en.wikipedia.org/.../Subspace_Gaussian_mixture_model

    Subspace Gaussian mixture model (SGMM) is an acoustic modeling approach in which all phonetic states share a common Gaussian mixture model structure, and the means and mixture weights vary in a subspace of the total parameter space. [1]

  8. GMM - Wikipedia

    en.wikipedia.org/wiki/GMM

    Gaussian mixture model, a statistical probabilistic model; Google Map Maker, a public cartography project; GMM, IATA code for Gamboma Airport in the Republic of the Congo; Good Mythical Morning, an online morning talk show hosted by YouTubers, Rhett and Link; Global Marijuana March, a worldwide demonstration associated with cannabis culture

  9. Model-based clustering - Wikipedia

    en.wikipedia.org/wiki/Model-based_clustering

    Model-based clustering [1] based 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 not ...