Stein's method has emerged as a powerful and versatile tool in probability theory for deriving error bounds in distributional approximations. Originally developed to ...

Continuous probabilistic techniques involving simulation can help managers predict the likelihood of time and cost overruns in all types and sizes of oil and gas projects. By deriving time and cost ...

A key issue in complex systems design is measuring the 'goodness' of a design, i.e. finding a criterion through which a particular design is determined to be the 'best'. Traditional choices in ...

The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data. A machine can use such models to make ...

The purpose of the Institute of Mathematical Statistics (IMS) is to foster the development and dissemination of the theory and applications of statistics and probability. The Institute was formed at a ...

What Is A Probabilistic Model? A probabilistic model is a statistical tool that accounts for randomness or uncertainty when predicting future events. Instead of giving a definitive answer, it ...

Sampling is a tool researchers use for marketing, sociology or empirical study. In order for sampling to be productive, the data analysis must not be tainted. There are techniques for creating a ...

SAN DIEGO, March 16, 2021 (GLOBE NEWSWIRE) -- GBT Technologies Inc. (OTC PINK: GTCH) ("GBT” or the “Company”) is researching Probabilistic Neural Network (PNN) algorithms and techniques for Kirlian ...

Which is better – deterministic data or probabilistic data? It’s a trick question because the answer is “both.” Even Google – whose first-party cross-device logged in user base likely trumps anything ...