Discover how probability distribution methods can help predict stock market returns and improve investment decisions. Learn ...

We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and ...

Abstract: In this paper, a novel mathematical framework based on Continuous Mixture of Uniforms (CMU) for modeling the Residual Lifetime physical phenomenon is proposed. Through an alternative and ...

Abstract: A major goal in recurrent events analysis is to estimate the bivariate distribution function. This estimation is crucial across various fields and applications, as it helps clarify the ...

What Is A Probability Density Function? A probability density function, also known as a bell curve, is a fundamental statistics concept, that describes the likelihood of a continuous random variable ...

All the scripts were developed on MATLAB 2023a (compatible with MATLAB 2022b or later). This file contains two main scripts: CoherentBasis.m FockBasis.m which define two types of classes. The first ...

Probability distribution is an essential concept in statistics, helping us understand the likelihood of different outcomes in a random experiment. Whether you’re a student, researcher, or professional ...

Experience is known to facilitate our ability to interpret sequences of events and make predictions about the future by extracting temporal regularities in our environments. Here, we ask whether ...