Learn about some of the most useful probability distributions and concepts for data science, such as the normal, binomial, and Poisson distributions, Bayes' theorem, PDF, and CDF.

Abstract: Fusing probabilistic information is a fundamental task in signal and data processing with relevance to many fields of technology and science. In this work, we investigate the fusion of ...

In this article, a novel data-driven predictive probability density function (PDF) control method is proposed for the FLSD shaping in the refining process. First, the PDF of FLSD shaping is ...

A probability density function (PDF) describes how likely it is to observe some outcome resulting from a data-generating process. A PDF can tell us which values are most likely to appear versus ...

The nonferrous metal smelting industry data collected between 1 March 2018 and 31 August 2018 are selected for a short-term probability density function forecasting case study. After converting a 1D ...

Objective To understand how often ‘breakthroughs,’ that is, treatments that significantly improve health outcomes, can be developed. Design We applied weighted adaptive kernel density estimation to ...