Sentence examples for probability of text from inspiring English sources

Going from the innermost bin to the outermost bin, the probability of text diminishes, and the probability of nontext increases.

Fig. 3 shows how the probability of (text {SINR}_{m}^{text {orth}} > {Gamma _{text {target}}}) changes with ρ and R m.

By extracting features from new texts to be classified, we can according to Bayes' theorem calculate the posterior probability of the text having a certain label (e.g., radical or non-radical) as: P ( label | f 1, …, f n ) = P ( label ) P ( f 1, …, f n | label ) P ( f 1, …, f n ).

In this section, we will suppose that the probability of each text is given by Bernoulli model.

One can apply the equations to compute probabilities of the text sets for arbitrary probability models.

To overcome this obstacle, we designed a new method for eliciting probabilities from experts that combines the ideas of transcribing probabilities as fragments of text and of using a scale with both numerical and verbal anchors for marking assessments.

Lower ten panels: sequence of observation states input to the BBN to estimate probability of eruption (see text).

The theoretical distributions were calculated up to the level of active neurons which occur with a cumulative probability of 0.9 (see main text).

In order to simplify or study, geometrical scenarios with two crossings between nullclines have been not considered due to their low probability of emergence (see main text).

A topic model is a probability distribution technique over the collection of text documents, where each document is modeled as a combination of topics, which represents groups of words that tend to occur together.

In these approaches the distance between two concepts is determined by the information content of their lowest common superordinate, which is inversely related to probability of occurrence in a body of text.