Sentence examples for functions to be estimated from inspiring English sources

However, in neuroscience, except in the particular case of the homogeneous Poisson process, there is no a priori parametric shape for the functions to be estimated.

It is the first time where the same model allows both classical cyclic electrogravimetry (current and mass over a potential scan) and ac-electrogravimetry (electrochemical impedance and mass/potential transfer functions) to be estimated theoretically.

If the implications of economic theory are described in terms of the shapes of functions implied by the theory (e.g. Engel curves) then any empirical investigation of the theory requires those functions to be estimated from data.

An important point in which GPR differs from linear regression, is that the method assumes a probability distribution over the set of functions to be estimated, which allows for determining families of regression functions with specific functional forms.

In this section, we define the functions to be estimated.

We show that special Poisson regression models allow hazard functions to be estimated as continuous functions, which makes it possible to derive and calculate probabilities, expected length of life and expected gain in QALYs.

In this context, h corresponds to the function to be estimated (f), according to the formalism of the previous section.

This method solves the inverse natural convection problem accurately without a priori information about the unknown function to be estimated.

It is an almost unbiased estimate of the expected error on unseen data, but requires the function to be estimated m times.

The proposed model generalizes the Bayesian dense deformable template model, a hierarchical model in which the template is the function to be estimated and the deformation is a nuisance, assumed to be random with a known prior distribution.

In the need for low assumption inferential methods in infinite-dimensional settings, Bayesian adaptive estimation via a prior distribution that does not depend on the regularity of the function to be estimated nor on the sample size is valuable.