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Essentially, BMA treats parameters and models as random variables and attempts to summarise the uncertainty about the model in terms of a probability distribution over the space of possible models.
If the tree-to-tree variation that is currently included in the models as random variation could be at least partially explained by attributes that could be readily measured on trees or logs, then the predictive power of models could be greatly improved.
Herd was included in the models as random effect.
In addition, participants were included in all models as random factors.
We included the quarter and trust in the regression models as random effects, to allow for temporal trends.
The subject, the object identity and the test sequence were added in all models as random factors.
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Fermentation run entered in the model as random effect.
All effects except m i were modeled as random.
All parameters are modeled as random walk processes by default.
Blocking was modeled as random sequential adsorption (RSA).
In this paper, the effect-sizes are modeled as random effects with t distributions.
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