A missing data analysis is performed using maximum-likelihood estimation, the expectation maximization (EM) algorithm, and the Kalman filter to fill in missing observations and regression parameters, ...

The algorithm for QTL analysis based on the maximization of AUC is also explained. The results of numerical examples serve to illustrate the properties and the validity of our method.

This project applies the Expectation-Maximization (EM) algorithm to estimate the relative abundances of RNA isoforms based on RNA-seq read data. The task involved modeling how sequencing reads map to ...

An advanced approach combines Deeplabv3+, a deep learning architecture for semantic segmentation, with Uncertainty Estimation and the Expectation Maximization (EM) algorithm to obtain increased ...

The conventional method for estimation of the parameters of Hidden Markov Model (HMM) based acoustic modeling of speech signals uses the Expectation- Maximization (EM) algorithm. But the EM algorithm ...

Olivier Cappé, Eric Moulines, On-Line Expectation-Maximization Algorithm for Latent Data Models, Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 71, No. 3 (Jun., ...

This article presents and examines a new algorithm for solving a score equation for the maximum likelihood estimate in certain problems of practical interest. The method circumvents the need to ...

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