Estimation of mating system parameters in plant populations using the EM algorithm

1983 ◽  
Vol 65 (2) ◽  
pp. 157-161 ◽  
Author(s):  
W. M. Cheliak ◽  
K. Morgan ◽  
C. Strobeck ◽  
F. C. H. Yeh ◽  
B. P. Dancik
Keyword(s):  
Em Algorithm ◽  
Mating System ◽  
Plant Populations ◽  
System Parameters ◽  
The Em Algorithm

Joint estimation of immigration and mating system parameters in gymnosperms using the EM algorithm

1991 ◽  
Vol 83 (2) ◽  
pp. 137-140 ◽  
Author(s):  
C. Y. Xie ◽  
F. C. Yeh ◽  
B. P. Dancik ◽  
C. Strobeck
Keyword(s):  
Em Algorithm ◽  
Mating System ◽  
Joint Estimation ◽  
System Parameters ◽  
The Em Algorithm

scholarly journals A multilocus estimator of mating system parameters in plant populations

1981 ◽  
Vol 78 (2) ◽  
pp. 1298-1302 ◽  
Author(s):  
D. V. Shaw ◽  
A. L. Kahler ◽  
R. W. Allard
Keyword(s):  
Mating System ◽  
Plant Populations ◽  
System Parameters

MONTE CARLO STUDIES OF PLANT MATING SYSTEM ESTIMATION MODELS: THE ONE-POLLEN PARENT AND MIXED MATING MODELS

Genetics ◽  
1986 ◽  
Vol 112 (4) ◽  
pp. 927-945
Author(s):  
Daniel J Schoen ◽  
Michael T Clegg
Keyword(s):  
Monte Carlo ◽  
Mating System ◽  
Bootstrap Method ◽  
Pollen Parent ◽  
Mixed Mating ◽  
Plant Populations ◽  
System Parameters ◽  
Monte Carlo Studies ◽  
The One ◽  
Estimation Models

ABSTRACT Estimation of mating system parameters in plant populations typically employs family-structured samples of progeny genotypes. These estimation models postulate a mixture of self-fertilization and random outcrossing. One assumption of such models concerns the distribution of pollen genotypes among eggs within single maternal families. Previous applications of the mixed mating model to mating system estimation have assumed that pollen genotypes are sampled randomly from the total population in forming outcrossed progeny within families. In contrast, the one-pollen parent model assumes that outcrossed progeny within a family share a single-pollen parent genotype. Monte Carlo simulations of family-structured sampling were carried out to examine the consequences of violations of the different assumptions of the two models regarding the distribution of pollen genotypes among eggs. When these assumptions are violated, estimates of mating system parameters may be significantly different from their true values and may exhibit distributions which depart from normality. Monte Carlo methods were also used to examine the utility of the bootstrap resampling algorithm for estimating the variances of mating system parameters. The bootstrap method gives variance estimates that approximate empirically determined values. When applied to data from two plant populations which differ in pollen genotype distributions within families, the two estimation procedures exhibit the same behavior as that seen with the simulated data.

EM Algorithm Based on Fuzzification and its Application

2012 ◽  
Vol 532-533 ◽  
pp. 1445-1449
Author(s):  
Ting Ting Tong ◽  
Zhen Hua Wu
Keyword(s):  
Em Algorithm ◽  
Mixed Model ◽  
Image Data ◽  
Training Sample ◽  
Model Parameters ◽  
Parameter Learning ◽  
Weighted Degree ◽  
The Em Algorithm ◽  
The Impact

EM algorithm is a common method to solve mixed model parameters in statistical classification of remote sensing image. The EM algorithm based on fuzzification is presented in this paper to use a fuzzy set to represent each training sample. Via the weighted degree of membership, different samples will be of different effect during iteration to decrease the impact of noise on parameter learning and to increase the convergence rate of algorithm. The function and accuracy of classification of image data can be completed preferably.

Sign in / Sign up

Export Citation Format

Share Document