Introduction: Moving Beyond Predictive Accuracy  Prediction has been traditionally the backbone of applied data science. From ...

Discover how probability distribution methods can help predict stock market returns and improve investment decisions. Learn to assess risk and potential gains.

Abstract: In this contribution, we employ the Mellin transform to derive the expressions for probability density function (PDF) of the product of Nakagami-m and Gamma distributed random variables. As ...

Interest Rate Probability Distributions Implied by Derivatives Prices is a daily measure of the distribution of future short-term interest rates, calculated from prices of fixed-income derivatives ...

Traditional probability-based structural reliability analysis method can only consider random uncertainties described by random distribution functions, which required sufficient experimental samples.

A courseware module that covers the fundamental concepts in probability theory and their implications in data science. Topics include probability, random variables, and Bayes' Theorem.

The study of specific physiological processes from the perspective of network physiology has gained recent attention. Modeling the global information integration among the separated functionalized ...

The FactorGraph package provides the set of different functions to perform inference over the factor graph with continuous or discrete random variables using the belief propagation algorithm. A ...