The EM algorithm is a very popular maximum likelihood estimation method, the iterative algorithm for solving the maximum likelihood estimator when the observation data is the incomplete data, but also ...

The expectation-maximization (EM) algorithm is an elegant algorithm that maximizes the likelihood function for problems with latent or hidden variables. As from the name itself it could primarily be ...

Haplotype inference is an indispensable technique in medical science, especially in genome-wide association studies. Although the conventional method of inference using the expectation-maximization ...

(i) generates some data from a mixture of Gaussians (MoG) model, and (ii) subsequently fit a MoG model to the generated data, in order to recover the original parameters using the EM algorithm. A ...

Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, USA. Compositional data exclusively consists of relative information. These entities are part of a broader entity.

Many disease resistance traits in plants have a polygenic background and the disease phenotypes are modified by environmental factors. As a consequence, the phenotypic values usually show a ...

Abstract: The ML-EM (maximum likelihood expectation maximization) algorithm is the most popular image reconstruction method when the measurement noise is Poisson distributed. This short paper ...

Abstract: The estimate-maximize (EM) algorithm is an iterative method for finding maximum-likelihood parameter estimates from incomplete data. The authors develop an extension of the EM algorithm that ...

EM algorithm failed: Backward probabilities contain non-finite values. I am unsure what is causing this for that specific dataset as all others are working without problems.