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For future research focusing on parameter inference, this procedure could be modified to use a larger number of simulated data sets and a lower tolerance rate.
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Running this inference procedure on a cluster of 12 Intel 3.33-GHz cores took ∼70 hr.
Because of additional information from the stochastic system model, this inference procedure is advantageous over the standard Bayesian procedure in that it can avoid the identifiability problems associated with standard Bayesian method.
At the end of this inference procedure, all background nodes are set to zero, and the foreground expression value is used as the final gene expression pattern obtained at the end of our image analysis pipeline.
This semantics captures and makes precise the two main intuitions underlying contextual reasoning: (i) reasoning is mainly local and uses only part of what is potentially available (e.g., what is known, the available inference procedures), this part is what we call context (of reasoning); however (ii) there is compatibility among the reasoning performed in different contexts.
We can see that, this procedure is a byproduct of the inference procedure and does not incur any additional computational cost.
In this case, the inference procedure needed more links to reproduce the simulated time series.
Usually, an inference procedure for this type of problem constructs a network that is to some extent consistent with the observed sequence.
This paper develops an inference procedure for the peak over threshold (POT) model, using semi-conjugate informative priors.
Although the network was inferred from these experiments, the inference procedure was applied to each gene individually.
A general probabilistic inference procedure is proposed in this paper based on the Maximum relative Entropy (MrE) approach which generalizes both Bayesian and Maximum Entropy (MaxEnt) inference methodologies.
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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