Probabilistic graphical models (PGMs) such as Bayesian network (BN) have been widely applied in uncertain causality representation and probabilistic reasoning. Dynamic uncertain causality graph (DUCG) ...

This table represents a discrete probability function, which shows the probability associated with each possible value of a discrete random variable. Such distributions can also be displayed ...

Filus, J.K. and Filus, L.Z. (2017) The Cox-Aalen Models as Framework for Construction of Bivariate Probability Distributions, Universal Representation. Journal of Statistical Science and Application, ...

Bayesian networks: representations (graph vs. probability distribution, independence assumptions), reading off independence assumptions (d-separation, etc.) Markov networks: representations, factors, ...

Since frequency pictograms are "the most common graphical representations of quantitative information," Jain's research has potentially wide applications.

Sampling entails the ability to extract samples from the underlying distribution as defined by the graphical model. A common challenge with graphical model representations lies in the high ...

A probability distribution, usually displayed graphically, shows the relative likelihood of all possible outcomes occurring within a specific time period.

Drawing is the act of replacing reality with representation, that is, replacing objects with images encoded in each of the graphic representation systems.