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All simulated parameters are given in Table S1.
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For all methods, genetic parameters were not re-estimated, but the true (simulated) parameters were used.
The estimates deviation from the simulated parameters was used to check for bias due to the pedigree depth and/or missing genotypes.
The goodness of fit of our simulated parameters was considered satisfactory if the mean of the experimental data is included within the standard deviation of the simulated parameter for 24 h.
The simulated parameters were: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\sigma}_{\mathrm{a}}^2=500,{\sigma}_{\mathrm{m}}^2=0,{\sigma}_{am}=0,{\sigma}_{\mathrm{s}}^2=250,{\sigma}_{\mathrm{e}}^2=1000.
The structure of the simulated network and simulation parameters are then presented.
A total of 100 data sets are simulated, and the model parameters are estimated using the FOCEI method.
In the following the 90 simulated parameter identifications are denoted as 90 experiments.
For a quantitative analysis, simulated parameter identifications are performed with 90 notional industrial manipulators.
First, simulated parameter values are obtained by drawing random values from the data set to obtain a new value of the parameter estimate.
First, simulated parameter values are obtained by drawing random values from the normal distribution of the estimated parameters using their variance co-variance matrix to obtain a new value of the parameter estimate.
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