scholarly journals A Maximum-Likelihood Formulation and EM Algorithm for the Protein Multiple Alignment Problem

2010 ◽  
pp. 171-182 ◽  
Author(s):  
Valentina Sulimova ◽  
Nikolay Razin ◽  
Vadim Mottl ◽  
Ilya Muchnik ◽  
Casimir Kulikowski
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Multiple Alignment ◽  
Alignment Problem

Maximum-likelihood estimation in linkage heterogeneity models including additional information via the EM algorithm

1995 ◽  
Vol 12 (5) ◽  
pp. 515-527 ◽  
Author(s):  
Jeanine J. Houwing-Duistermaat ◽  
Lodewijk A. Sandkuijl ◽  
Arthur A. B. Bergen ◽  
Hans C. van Houwelingen
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Em Algorithm ◽  
Likelihood Estimation ◽  
Additional Information ◽  
The Em Algorithm

scholarly journals Generalized Modified Slash Birnbaum–Saunders Distribution

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 724 ◽  
Author(s):  
Jimmy Reyes ◽  
Inmaculada Barranco-Chamorro ◽  
Diego Gallardo ◽  
Héctor Gómez
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Standard Normal Distribution ◽  
Stochastic Representation ◽  
Moments Method ◽  
Standard Normal ◽  
Newton Raphson ◽  
New Distribution ◽  
Skewness And Kurtosis ◽  
Modified Moments

In this paper, a generalization of the modified slash Birnbaum–Saunders (BS) distribution is introduced. The model is defined by using the stochastic representation of the BS distribution, where the standard normal distribution is replaced by a symmetric distribution proposed by Reyes et al. It is proved that this new distribution is able to model more kurtosis than other extensions of BS previously proposed in the literature. Closed expressions are given for the pdf (probability density functio), along with their moments, skewness and kurtosis coefficients. Inference carried out is based on modified moments method and maximum likelihood (ML). To obtain ML estimates, two approaches are considered: Newton–Raphson and EM-algorithm. Applications reveal that it has potential for doing well in real problems.

scholarly journals Direct fast estimator for recombination frequency in the F2 cross

2021 ◽  
Author(s):  
Mathias Lorieux
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Maximum Likelihood Estimator ◽  
Recombination Frequency ◽  
Short Note ◽  
Simulation Studies ◽  
Likelihood Estimator ◽  
Simple Estimate ◽  
Efficient Simulation

AbstractIn this short note, a new unbiased maximum-likelihood estimator is proposed for the recombination frequency in the F2 cross. The estimator is much faster to calculate than its EM algorithm equivalent, yet as efficient. Simulation studies are carried to illustrate the gain over another simple estimate proposed by Benito & Gallego (2004).

Maximum likelihood noise cancellation using the EM algorithm

1989 ◽  
Vol 37 (2) ◽  
pp. 204-216 ◽  
Author(s):  
M. Feder ◽  
A.V. Oppenheim ◽  
E. Weinstein
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Noise Cancellation ◽  
The Em Algorithm
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