FME stands for "Fundamental Measure of Error" and is a statistical approach used to measure the difference between two probability distributions.
Continuous distributions have an infinite number of possible values, while discrete distributions have a finite number of possible values. Additionally, continuous distributions have a probability density function, while discrete distributions have a probability mass function.
FME is used to compare and evaluate the accuracy of different probability distributions. It can help determine which distribution best fits a set of data.
Comments are crucial when using FME in probability as they provide insight into the reasoning behind certain decisions and help others understand and replicate the results. They also allow for transparency and reproducibility in scientific research.
Yes, FME can be applied to both continuous and discrete distributions, as long as there is a quantifiable measure of error between the two distributions. However, the interpretation of the results may differ slightly between the two types of distributions.