Sampling from a probability distribution is a fundamental task in machine learning. It allows you to generate synthetic data, estimate parameters, test hypotheses, and perform inference.

Probability distributions can also be used to create cumulative distribution functions (CDFs) that add up the probability of occurrences cumulatively. They always start at zero and end at 100%.

However complicated such a compound probability function may be, it is straightforward to show that the function describes a lognormal distribution. In structure, the probability function (27) ...

Z. A. Lomnicki, On the Distribution of Products of Random Variables, Journal of the Royal Statistical Society. Series B (Methodological), Vol. 29, No. 3 (1967), pp. 513-524. ... Similar formulae for ...

The parameters in the joint probability distribution functions of heatwave severity and duration were estimated using the maximum likelihood method. The goodness-of-fit tests were performed based on ...

In this paper, an approximate scheme called the Unscented Transforms (UT) method is proposed for probabilistic load flow studies. The proposed method involves carefully choosing a few selected points ...

We previously derived the probability distribution function (PDF) of the co-channel interference (CCI) and effective noise (EN) for the detection in a research work, and the PDF is utilized in this ...