Sentence examples for inference discussed from inspiring English sources

Our work provides two novel contributions over current approaches to gene network inference discussed in Related work: FuseNet simultaneously infers networks from datasets that may be generated by nonidentical distributions, and FuseNet estimates large-scale genomic networks from increasingly common non-Gaussian distributed data.

A general AC-Inference algorithm schema is presented and various forms of inference discussed.

Aren't the methods of causal inference discussed in Section 2 sufficient as a methodology for experimental biology (see Rappaport 1996)?

Note that recent work in the statistical literature uses the quantity α = 1/k due to its preferable properties for inference (discussed below), but studies applying the NB distribution in ecology and epidemiology are overwhelmingly posed in terms of k.

Compared to the parameter inference problem discussed in the previous three sections, the phylogeny inference problem is significantly more difficult.

Theoretical and practical issues related to model description tools, diagnosis processing structure, sensor selection, fault representation and inference are discussed.

For design issues, sampling and generalizabilty as well as randomized and quasi-experimental designs for causal inference are discussed.

The pros and cons of spectral Bayesian inference are discussed and demonstrated on the basis of simple applications from classical statistics and inverse modeling.

In Section  3, semi-parametric likelihood inference is discussed.

The parameters of the benchmark network inference methods discussed here were set to generate networks of sizes (34 nodes) comparable to those obtained with our approach.

The article is organized as follows: In the first two sections of the Discussion part, the counterfactual model of causal effects is defined, and some general aspects on statistical inference are discussed.