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

Stochastic volatility is the unpredictable nature of asset price volatility over time. It's a flexible alternative to the Black Scholes' constant volatility assumption.

Where \(X\) is a normally distributed random variable with mean \(\mu\) and standard deviation \(\sigma\). The peak of the curve occurs at \(x=\mu\), and the spread ...

A discrete random variable is a type of random variable that can take on a countable set of distinct values. Common examples include the number of children in a family, the outcome of rolling a die, ...

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.

Future events are far from certain in the business world. This is especially true for smaller businesses, which tend to have more volatility than larger organizations, or newer businesses without a ...

Forecasting for any small business involves guesswork. You know your business and its past performance, but you may not be comfortable predicting the future. Using Excel is a great way to perform what ...

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 ...

Abstract: We derive the exact probability density functions (pdf) and distribution functions (cdf) of a product of n independent Rayleigh distributed random variables. The case n=1 is the classical ...