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This is in contrast to alternative techniques (e.g., the Extended Kalman filter; EKF) that approximate the models such that the uncertainty present can be approximated using a parametric probability density (a multivariate Gaussian in the case of an EKF).
This pattern was similar whether using the observed serial interval distribution from the outbreak data or using a parametric probability distribution.
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Using a parametric file for SCAD, you can easily recreate a Kwikset key with a few keystrokes.
As suggested by Kass et al. (2005) and Brown et al. (2004), instead of assuming a parametric probability distribution (as used, for example, in Bayesian decoding schemes) we use direct bootstrapping to obtain non-parametric distributions.
A parametric probability distribution could also be used to express the analysts' uncertainties, for Z1 and X.
There are two kinds of pattern identification unit: One uses a parametric method for which the probability distribution of data is known in advance and another uses a nonparametric method which requires collected data because the probability distribution of data is unknown.
The method proposed in Sect. 4 does not assume a parametric probability distribution for spike trains and it also gives a bound for the response probability.
EBarrays uses a parametric mixture model to calculate the posterior probability of differential expression for arbitrarily complex experimental designs.
We used a parametric Markov chain model to estimate the probabilities of cognitive improvement, decline, and death, adjusted for age and education, from any cognitive state as measured by the Modified Mini-Mental State Examination.
To estimate the probability of transcripts appearing to be bimodally expressed due to chance alone we used a parametric bootstrapping method.
Here, we use a parametric 3D shape-space to test whether adaptation decorrelates shape representations in humans.
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