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From the area of highest posterior density in the posterior distribution of each model parameter we obtained Bayesian credible intervals (CI) that defined a 95% probability of a given parameter lying within the CI range.

This means that for each perturbation of an original model parameter, we constructed a new hybrid model and examined the analytical expressions determining essential and modulatory effects.

For each model parameter, we compare the dispersion within each sample to the total dispersion obtained by concatenating the ten samples.

To further study the effect of varying the model parameter, we calculated an "error" which quantifies the difference between simulated and observed entropy profiles (see Methods).

25 For each model parameter, we assigned a lower and upper value using 95% confidence intervals where available or ±20% values (for numbers of patients, uptake of treatment, and compliance).

For each model parameter, we counted the number of times a perturbation caused the loss of a phenotype, and ranked parameters according to their ability to affect our objective function (the total number of captured phenotypes).

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To estimate the model parameters, we use real-world data on the 2013 midterm elections in Argentina.

By adjusting model parameters, we manipulated the 'intention' of VP as cooperative or competitive with the human's instructed goal.

In order to efficiently estimate the model parameters, we derive two scalable co-clustering algorithms based on variational inference.

To effectively learn the model parameters, we automatically collect large-scale mention-entity pairs from Wikipedia as training data.

In this analysis we also obtain Maximum a posteriori (MAP) estimates of the model parameters; we find the following mean values from populations of MAP estimates with constant λ: λc = 15.3 ± 1.7 μm (9l); λc = 19.3 ± 1.9 μm (U118); λc = 16.2 ± 1.9 μm (MCF7).

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