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Finally, some methods for probability density function estimation were also considered.
The basis of this novel strategy is inspired from the k-Nearest Neighbours (kNN) approach and adaptive kernel methods for probability density estimation (kernel density estimators, KDE) [11].
Our response-averaging model therefore provides the same consequence in terms of firing rates as the model by Reynolds et al. We finish by addressing the possible model selection methods for probability mixing and response averaging on real data.
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Importance sampling as a technique to improve the Monte Carlo method for probability integration can be shown to be extremely efficient and versatile.
In this work, a non-parametric method for probability density function estimation based on kernel density estimation [7] is used.
Inspired from the adaptive kernel method for probability density function estimation, the first stage of the approach defines a pattern of thresholds corresponding to the various training samples and these thresholds are later used to derive the decision rule.
Probabilistic Fracture Mechanics combines the mathematical methods for failure probability calculation of structural reliability assessment with the fracture mechanics failure description of crack-containing structures.
The purpose of this study was to develop methods for exceedance probability estimation in the case of highly scattered measurement sets.
The existing analytical and numerical computing methods for collision probability (Pc) provide sufficiently accurate results, but do not reveal the direct connection between Pc and conjunction geometry or error covariance.
To this end, we will use more rigorous methods for analyzing probability distributions than we used so far.
A comparison of the enhanced Good-Turing and deleted estimation methods for estimating probabilities of English bigrams.
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