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  2. Expectation–maximization algorithm - Wikipedia

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

    Obtaining this Q-function is a generalized E step. Its maximization is a generalized M step. This pair is called the α-EM algorithm [38] which contains the log-EM algorithm as its subclass. Thus, the α-EM algorithm by Yasuo Matsuyama is an exact generalization of the log-EM algorithm. No computation of gradient or Hessian matrix is needed.

  3. 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.

  4. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    This training algorithm is an instance of the more general expectation–maximization 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. The algorithm is formally justified by the assumption that the data are generated by a mixture model, and the components of this ...

  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 expectation–maximization 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. Two-step M-estimator - Wikipedia

    en.wikipedia.org/wiki/Two-step_M-estimator

    When the first step is a maximum likelihood estimator, under some assumptions, two-step M-estimator is more asymptotically efficient (i.e. has smaller asymptotic variance) than M-estimator with known first-step parameter. Consistency and asymptotic normality of the estimator follows from the general result on two-step M-estimators. [4] Let {V i ...

  7. Golden-section search - Wikipedia

    en.wikipedia.org/wiki/Golden-section_search

    The two interval lengths are in the ratio c : r or r : c where r = φ − 1; and c = 1 − r, with φ being the golden ratio. Using the triplet, determine if convergence criteria are fulfilled. If they are, estimate the X at the minimum from that triplet and return. From the triplet, calculate the other interior point and its functional value.

  8. St. Petersburg city council reverses course on Tampa Bay Rays ...

    www.aol.com/sports/st-petersburg-council...

    Hours after the St. Petersburg City Council approved spending $23.7 million to repair Tropicana Field after it was damaged during Hurricane Milton, the council reversed its decision in a second vote.

  9. Viterbi algorithm - Wikipedia

    en.wikipedia.org/wiki/Viterbi_algorithm

    Viterbi path and Viterbi algorithm have become standard terms for the application of dynamic programming algorithms to maximization problems involving probabilities. [3] For example, in statistical parsing a dynamic programming algorithm can be used to discover the single most likely context-free derivation (parse) of a string, which is ...