Sentence examples for inference methodologies from inspiring English sources

We review recent Bayesian network inference methodologies we developed to infer genetic and metabolic pathways associated to oncological drug chemoresistance.

The incorporation of new information from monitoring and/or testing will be performed by the application of Bayesian inference methodologies.

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.

The main focus is analytical methods, although numerical methods and statistical inference methodologies for studying diffusion processes are also presented.

Secondly, we propose to study statistical inference methodologies for bivariate (and, more generally, multivariate) BG distributions in (9) and (12).

Here, we test one fundamental difference between stochastic mapping and maximum parsimony inference methodologies.

Such model was inferred from experimental data and the, InferGene inference methodology [ 15], which is used to obtain kinetic parameters from experimental steady-state data.

The M-LaF model parameters are calibrated as posterior distribution using the Bayesian inference methodology.

Variational Bayes is an approximate Bayesian inference methodology that is increasingly popular due to its fast performance on large datasets.

One of the applications of the BRB is the Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER).

Here we present a novel parametric inference methodology for problems with linear parameter dependency that does not suffer from this drawback.