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PROBABILITY DENSITY FUNCTION LEARNING BY UNSUPERVISED NEURONS

International Journal of Neural Systems, 2001
In a recent work, we introduced the concept of pseudo-polynomial adaptive activation function neuron (FAN) and presented an unsupervised information-theoretic learning theory for such structure. The learning model is based on entropy optimization and provides a way of learning probability distributions from incomplete data.
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Extended sinepower probability density function

Journal of Hydrology, 1978
The author developed a new probability density function to fit random data, bounded on both extremes and having a single mode within its range of values. This function called sinepower probability density function (SP-PDF) has now been extended to include cases of minimum value having a non-zero probability.
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Calibration of Probability Density Function

2019
The capability performance index (Cpk) is often used to measure the capability of the production process and to predict yield. However, this Cpk is only defined for the Gaussian distribution. At NXP Semiconductors an on-chip calibration technique is frequently used to reduce the effect of process variations.
Jos J. Dohmen   +5 more
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Conditional Probability Density Functions

2012
A discussion of conditional probability mass functions (PMFs) was given in Chapter 8. The motivation was that many problems are stated in a conditional format so that the solution must naturally accommodate this conditional structure. In addition, the use of conditioning is useful for simplifying probability calculations when two random variables are ...
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Appendix A: Probability Density Functions

2009
This chapter contains sections titled: Relationship Between PDF and CDF PDFs Used in This Book Central Limit Theorem Generation of Standard Normal Random ...
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Probability Density Function for Clustering Validation

2023
Pau Figuera   +2 more
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