In this section, we propose to generalize this idea to any estimation problem where model parameters have to be estimated from a perturbed observed signal.
The time of ovulation can be estimated from a calendar record of previous menstruation, but this method has low effectiveness.
Depth can be estimated from a single pair of images by a stereo vision technique.
If the liquid is known, the liquid depth can be estimated from a capacitance measurement.
First, the atomic coordinates must be estimated (from a known structure) or fitted to the image.
Both the probabilistic response and its sensitivities can be estimated from a single stochastic analysis, without requiring performance function gradients.
The process mean is rarely known and it has to be estimated from a Phase-I dataset.
This increase could be estimated from a weighted-volume average of the densities of PCE and the surfactant solution.
shall be estimated from a set of observations h ⌣ = [ h ⌣ 0, h ⌣ 1, …, h ⌣ L ] T (23).
We introduce the influence field, which can be estimated from a network of binary sensors, as the basis for a novel classifier.
We mention how probabilities can be estimated from a given data set and how a probability distribution can be approximated using a fuzzy histogram technique.
