Sentence examples for model based probabilities from inspiring English sources

Model based probabilities for liver tests exceeding predefined thresholds can support identification of potential liver safety signals even with rather small sample sizes, e.g. predicting incidence and magnitude of outliers in phase III studies or post marketing based on phase II data.

Furthermore, there is compression based on context-free grammars [ 28], determination of Markov-model based probabilities of sequence positions [ 29], and hybrid methods [ 30].

A new switched model based on probability is proposed to research problems of reliable control when actuators become ageing or partially disabled.

A perceived noisiness model based on probability summation is presented first followed by details on the contrast sensitivity thresholds computation.

In that study, the ratings of expectedness of the participants highly correlated with the estimation of a model based on the probabilities [47] and the ERP and neural oscillatory activities were clearly distinguished by the probabilities as well.

Quantum mechanics diverges from classical physics by replacing of a deterministic model of the universe with a model based on objective probabilities.

For turbulence-chemistry interaction, a model based on transported probability density function (TPDF) in conjunction with Eulerian stochastic fields is used.

Comparing the principles and results of the examples with the traditional failure possibility, the proposed model based on the probability perspective utilizes available information more adequately and the results are much closer to the engineering practices, while the traditional approach is too conservative.

Hence, Diehoid financial principle 'Assessment of density distribution model based on series probability integral transformation' is introduced [25, 26].

According to the characters of the network, network survivability is generally use as survivability measurement as the basis and establishes the survivability analysis model based on the probability theory and graph theory, etc.

The model estimators described above take aligned sequences as input and select the most appropriate model based on the probability (the likelihood) of the ML tree.