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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.
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 maximization–maximization procedure ...
The algorithm uses several types of well known functions: Expectation maximization (EM). EM based heuristic for choosing the EM starting point. Maximum likelihood ratio based (LRT-based) heuristic for determining the best number of model-free parameters. Multi-start for searching over possible motif widths. Greedy search for finding multiple ...
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 ...
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 ...
A general objective function is optimized during the simulation, most generally the "sum of pairs" maximization function introduced in dynamic programming-based MSA methods. A technique for protein sequences has been implemented in the software program SAGA (Sequence Alignment by Genetic Algorithm) [37] and its equivalent in RNA is called RAGA ...
Also, the convergence of the algorithm in higher dimensions with a finite number of the stationary (or isolated) points has been proved. [5] [7] However, sufficient conditions for a general kernel function to have finite stationary (or isolated) points have not been provided. Gaussian Mean-Shift is an Expectation–maximization algorithm. [8]
Statistical, likelihood-based approaches: Statistical, likelihood-based iterative expectation-maximization algorithms [7] [8] are now the preferred method of reconstruction. Such algorithms compute estimates of the likely distribution of annihilation events that led to the measured data, based on statistical principle, often providing better ...