An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data

Dynamic Data Rectification Using the Expectation Maximization Algorithm

Although on-line measurements play a ®ital role in process control and monitoring ( process performance, they are corrupted by noise and occasional outliers such as noise ) spikes . Thus, there is a need to rectify the data by remo®ing outliers and reducing noise effects. Well-known techniques such as Kalman Filtering ha®e been used effecti®ely to filter noise measurements, but it is not design...

An Expectation-Maximization Algorithm Working on Data Summary

Scalable cluster analysis addresses the problem of processing large data sets with limited resources, e.g., memory and computation time. A data summarization or sampling procedure is an essential step of most scalable algorithms. It forms a compact representation of the data. Based on it, traditional clustering algorithms can process large data sets efficiently. However, there is little work on...

Expectation Maximization Deconvolution Algorithm

In this paper, we use a general mathematical and experimental methodology to analyze image deconvolution. The main procedure is to use an example image convolving it with a know Gaussian point spread function and then develop algorithms to recover the image. Observe the deconvolution process by adding Gaussian and Poisson noise at different signal to noise ratios. In addition, we will describe ...

The Expectation Maximization Algorithm

This note represents my attempt at explaining the EM algorithm (Hartley, 1958; Dempster et al., 1977; McLachlan and Krishnan, 1997). This is just a slight variation on TomMinka’s tutorial (Minka, 1998), perhaps a little easier (or perhaps not). It includes a graphical example to provide some intuition. 1 Intuitive Explanation of EM EM is an iterative optimizationmethod to estimate some unknown ...

Expectation-Maximization (EM) algorithm