Sentence examples for inference compared from inspiring English sources

In this paper, we propose a modular Bayesian network system to extract context information by cooperative inference of multiple modules, which guarantees reliable inference compared to a monolithic Bayesian network without losing its strength like the easy management of knowledge and scalability.

This was experimentally shown to better facilitate the BN inference, compared to discretization with constant step or other common discrete distributions like gaussian and poisson.

The expected difference between context conditions was found, with 80% of participants in the Context condition making a bomb-relevant inference compared to only one participant from the No-context condition doing so (X 2 (1, N = 85) = 51.59, p < 0.001).

This paradigm also focuses on a collaborative and iterative style of work and an abductive mode of inference, compared to practices associated with the more traditional management paradigm.

Model averaging accounts for model uncertainty and provides more robust inference compared with a single optimal model approach (Ball, 2001; Raftery et al., 1997; Sillanpää and Corander, 2002).

By inference, compared to others with lower levels of hypomanic experience, stressful events are likely to have a greater impact on this population in daily life.

Use of GFP experimental data resulted in a relatively small set of duplicated gene pairs (128), from a genome-wide perspective, which makes it more difficult to infer a percentage of relocalized duplicates, but provided an increased confidence in localization inferences compared with localization prediction programs.

We study the effect of intelligence (General Mental Ability) on religiosity using research designs that allow for stronger causal inferences compared to previous research in this area.

Tesla P4 offers 40X faster inferencing compared to CPU.

If the amount of missing data is not insignificant, then multiple imputation will be used to evaluate sensitivity to the missing completely at random assumption, and inferences compared to those under the lesser missing at random assumption.

A key advantage of variational Bayesian inference algorithms compared to inference algorithms based on sampling is the dramatic improvement in time complexity of the algorithm.