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Both the model evidence and the posterior moments are related to the expansion coefficients.
Algorithms for computing output sensitivities to parameter variances, model evidence and posterior model plausibilities for given data, and for computing what are referred to as Occam Categories in reference to a rough measure of model simplicity, make up components of the overall approach.
Here, I use the Log Bayes Factor (LBF) as a measure of relative model evidence, where a smaller LBF indicates better model evidence, and in the present context this means higher predictive validity.
For purely pragmatic reasons of convenience one might still accept the model with formally fewer parameters even though it has the same model evidence and same effective degrees of freedom as a more complex one.
This approach has proved superior to the MNM approach, in terms of localization error and model evidence in simulations [Friston et al., 2008], and in terms of model evidence and the plausibility of source reconstructions in real data [Henson et al., 2009a].
In short, the concept of a 'network' and the form they take depends on the assumptions made in the data decomposition approach, and none should be considered necessarily better than the other (assuming they show similar objective performance, e.g., Bayesian model evidence and reproducibility), but instead they each offer different perspectives on the nature of the brain's activity.
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Using an Information-Theoretic approach for model-averaging and inference, we compared and interpreted the magnitude and direction of model-averaged coefficients, model evidence ratios, and overall fit of model-averaged predictions.
We use a recently developed PSLE, the ProNIFA system, to illustrate the concepts of student model, evidence model, and task model, concepts that provide for a close link between problem solving teaching software and assessment concepts.
For all the other datasets, model evidence, DIC and AIC favoured the correct model.
Clinical and animal model evidence indicates that estrogen and androgens like DHEA promote gland cell survival and protect against exocrine gland inflammation; and these hormone levels decline at menopause.
In theory, however, there are other source distributions (e.g., extensive and superficial activities) where MNM can have a higher model evidence than MSP, and in which fMRI priors may remain useful.
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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