Heitsch and Romisch [15] proposed a kind of fast forward and backward reduction to reduce the computational complexity, applying the discretization of possibility distribution instead of the initial continuous

probability distribution.

A

probability distribution is a statistical model that shows the possible outcomes of a particular event or course of action as well as the statistical likelihood of each event.

The solved system calculates all network node voltage

probability distributions which can be analyzed for power quality.

All pixel classifiers designed within this research are constructed as Bayes type classifiers based on corresponding

probability distributions.

Key words: Goodness-of-fit Test, L-Moments,

Probability Distributions, Quantile Estimates, Return Period, TL-moments.

16], during the simulation process, random scenarios are built up using input values for the project's key uncertain variables, which are selected from appropriate

probability distributions, and after which the results are collected and analyzed statistically so as to arrive at a

probability distribution of the potential outcomes of the project, and to estimate various measures of project risk.

the posterior is a joint

probability distribution in a high-dimensional parameter space.

Error is introduced by inaccurate mapping, PCR recombination, and chimeric ligation during library prep, however, so the calculated

probability distributions will shift toward 50% and broaden.

Out of the dozens of possible

probability distributions, the three used most often in capital budgeting simulations are rectangular (or continuous uniform) distribution, normal distribution, and triangle distribution.

Representing the joint

probability distribution as a directed graphical network simplifies the computation of the joint distribution by expressing it as a product of the conditional

probability distributions at every node using Bayes' rule.

A Bayesian Network is a means for clear and concise expression of combined

probability distribution among variables and interrelated assumptions.

Then it introduces both discrete and differential entropy and discusses challenges associated with interpreting and deriving the latter for various

probability distributions.