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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/Expectationmaximization...

    Itself can be extended into the Expectation conditional maximization either (ECME) algorithm. [35] This idea is further extended in generalized expectation maximization (GEM) algorithm, in which is sought only an increase in the objective function F for both the E step and M step as described in the As a maximizationmaximization procedure ...

  4. MM algorithm - Wikipedia

    en.wikipedia.org/wiki/Mm_algorithm

    The expectationmaximization algorithm can be treated as a special case of the MM algorithm. [1] [2] However, in the EM algorithm conditional expectations are usually involved, while in the MM algorithm convexity and inequalities are the main focus, and it is easier to understand and apply in most cases. [3]

  5. Baum–Welch algorithm - Wikipedia

    en.wikipedia.org/wiki/Baum–Welch_algorithm

    In electrical engineering, statistical computing and bioinformatics, the Baum–Welch algorithm is a special case of the expectationmaximization algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step. The Baum–Welch ...

  6. Expectation Maximisation - Wikipedia

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

    Download as PDF; Printable version; In other projects Appearance. ... Redirect page. Redirect to: Expectationmaximization algorithm; Retrieved from "https: ...

  7. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    This training algorithm is an instance of the more general expectationmaximization algorithm (EM): the prediction step inside the loop is the E-step of EM, while the re-training of naive Bayes is the M-step.

  8. Determining the number of clusters in a data set - Wikipedia

    en.wikipedia.org/wiki/Determining_the_number_of...

    The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]

  9. Bayesian network - Wikipedia

    en.wikipedia.org/wiki/Bayesian_network

    Direct maximization of the likelihood (or of the posterior probability) is often complex given unobserved variables. A classical approach to this problem is the expectation-maximization algorithm , which alternates computing expected values of the unobserved variables conditional on observed data, with maximizing the complete likelihood (or ...