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Since the model is probabilistic, it allows estimating local uncertainty conditionally to the available samples.
Since the model is probabilistic, each run generated different results due to chance.
The model is probabilistic in nature, with risk factors increasing the probability of the emergence of a learning disability and protective factors decreasing this probability.
Our model is probabilistic because it works with the probability density functions of the different inputs related to the natural history or detection of BC.
The "equiprobable rows, equiprobable columns" (EE) null model is probabilistic and assumes that the probability of a connection occurring between two nodes is related to the number of total connections in the input matrix.
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Because some of the parameters of our model are probabilistic in nature and therefore dimensionless, it is necessary to map them onto physical quantities in order to physically interpret the results.
All tests for finite populations were repeated ten times, the results averaged and the standard deviation recorded, since all finite population models are probabilistic.
To capture parameter uncertainty, input variables in our model are probabilistic values.
The model was probabilistic: input parameters were entered into the model as probability distributions to reflect uncertainty surrounding the mean estimates.
The model was probabilistic, that is, uncertainty over the input parameters was propagated through the model in such a way that the results of the analysis could be presented with their associated uncertainty [ 12].
Mixture models are probabilistic models that represent a complex distribution based on a linear combination of individual sub-distributions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com