Sentence examples for uncertainty class from inspiring English sources

The intrinsically Bayesian robust (IBR KLL compression minimizes the expected MSE over the uncertainty class among all possible m-term KL expansions.

We prove that the IBR KL compression is the KL expansion based on the expected covariance matrix over the uncertainty class.

The uncertainty class is defined as the union of four sub-classes: unknown disturbance, parametric uncertainty, measurement errors and neglected dynamics.

This paper considers the situation in which an unknown covariance matrix belongs to an uncertainty class governed by a prior probability distribution.

In [25], the problem of robust waveform design for distributed multiple-radar systems based on LPI is studied, in which the true target spectrum is presumed to lie within an uncertainty class bounded by known upper and lower bounds.

The IBR KL expansion and optimal experimental design are solved analytically for the Wishart distribution over the uncertainty class, a commonly employed distribution for covariance matrices in Bayesian settings.

We derive approximate expressions for the first and second moments of the true error rate of the proposed classifier under the assumption of two widely used models for the uncertainty classes: ε-contamination and p-point classes.

These probabilities, ranging from 0.25 (membership equally likely to all classes) to 1 (no uncertainty in class membership) were computed from the posterior distributions resulting from the Bayesian geostatistical model.

LCA models are useful where subtypes are sought and one wishes to model uncertainty surrounding class membership, since observations may belong to all classes, with probabilities determined empirically.

Fractions of a given subject may thus be allocated to different classes, meaning that analyses correctly adjust for the uncertainty in class membership.

To account for uncertainty in class membership the model assigns each individual a probability of class membership.