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A calibration dataset was used for fitting the coefficients of the G-R model, and for calculating a global variance: prediction error ratio which was then used to re-scale the weights of the B-G model.
We next analyzed variance prediction to understand important experimental and subject factors of gene expression differences in our dataset.
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Therefore, both mean and variance predictions are required.
Optimal sample sizes are calculated from variance predictions by Ospats.
All SNPs and small indels were validated by the experiment, excluding one position with a homo/hetero difference, suggesting that our variance predictions were highly accurate (Additional file 1: Table S2).
Two kinds of paralysis illustrate pathologies of the two types of descending projections in predictive coding networks: those mediating predictions, and those mediating precision (inverse variance of prediction error) or synaptic gain.
For each replicate the empirical mean and variance of prediction errors (true minus predicted breeding value) were computed within generation for animals with simulated uncertainty.
The focus now centres on specifying an I-optimal design for minimising the variance of prediction from fitting observations to model (7).
Indeed, the computational complexity of the proposed method is roughly the same as the classic minimum variance linear prediction analysis approach.
Perhaps the most important observation from this evaluation is the large variance in prediction accuracies across all methods.
Variance in prediction was basically eliminated when a large (≥25) number of thinned maps were averaged in TAGGING.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com