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The GPR algorithm provides a tractable and flexible hierarchical Bayesian framework for inferring the posterior distribution of streamflows.
We present a comprehensive neural network based modeling and validation framework for inferring regulatory interactions from temporal gene expression data.
We develop an optimization framework for inferring parameters of the logistic functions for a given patient population via cyclic block coordinate descent.
The Bayesian approach provides a consistent framework for inferring model parameters and comparing hypotheses.
In this paper we develop a maximum likelihood framework for inferring the best tree topology under (general) Markov processes.
A computational framework for inferring gene regulatory networks (Network-Identifier) was developed based on thermodynamic modeling of transcription regulation.
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The SGD method proposed in the paper presents a general framework of inferring parameters for stochastic kinetic models.
We describe here a computational framework, called Network-Identifier, for inferring regulatory networks from time course gene expression data.
We will however see that the Bayesian inference framework gives naturally more tools for inferring the uncertainty of the computed solutions, for the estimation of the hyperparameters or for handling the myopic or blind inversion problems.
To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks.
Figure 2 illustrates the general framework of inferring MRMs/FMRMs.
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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