Sentence examples for posterior confidence from inspiring English sources

For a given unlabeled instance, u j, the classifier generates the posterior confidence w.r.t.

This distance, d, is transformed into p∈[0,1] representing the posterior confidence of the learner on class +1.

The theoretical causal mechanism is confirmed after passing 19/20 empirical tests, which raises the posterior confidence up to 0.73 according to the Bayesian formalization.

In all the experiments, in both phases, we have compared our d-Confidence proposal against its baseline selection criteria: the common confidence active learning setting where query selection is solely based on low posterior confidence of the current classifier and farthest-first where query selection is based only on distance farthest-first whereces which is independent from the base classifier.

At iteration i, U i is the (set) difference between W and L i ; h i represents the classifier learned at iteration i; q i is the query selected at iteration i; conf i (u j,c k ) is the posterior confidence on class c k given instance u j, at iteration i.

From this, we can obtain the posterior confidence intervals for the direction of base substitution rates given in Table 3.

D-Confidence selects queries based on a criterion that aggregates the posterior classifier confidence and the distance between queries and known classes.

Depending on reconstruction methodology, such values may represent a posterior probability, confidence metric or parsimony score.

In some cases, estimation of the 95% highest posterior density confidence intervals was not possible given the low node support.

We used the R package "languageR" (Baayen 2008) to estimate p-values for the fixed effect of atmospheric CO2 concentration level in each model using Markov Chain Monte Carlo resampling and to generate highest posterior density confidence intervals (CIs) for means at each atmospheric CO2 concentration level.

Morphosyntactic tagging, using a tagger specifically designed for ASR transcripts, is used in a postprocessing stage to generate a final transcription with word-posterior-based confidence measures, combining the acoustic, language model, and morphosyntactic scores [17].