Probability distributions are fundamental tools in statistics and data science, allowing us to model the likelihood of different outcomes in a random event. While we often work with complete and ...

A new technical paper titled “Non-ideal subthreshold swing in aligned carbon nanotube transistors due to variable occupancy discrete charge traps” was published by researchers at Lawrence Berkeley ...

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, ...

The binomial distribution is a key concept in probability that models situations where you repeat the same experiment several times, and each time there are only two possible outcomes—success or ...

Abstract: It is well known that the entropy H(X) of a discrete random variable X is always greater than or equal to the entropy H(f(X)) of a function f of X, with equality if and only if f is ...

Discrete structures are omnipresent in mathematics, computer science, statistical physics, optimisation and models of natural phenomena. For instance, complex random graphs serve as a model for social ...

Abstract: We explore properties and applications of the principal inertia components (PICs) between two discrete random variables X and Y. The PICs lie in the intersection of information and ...

In the world of probability theory and statistics, conditional distribution is an essential concept that helps understand the relationship between two or more events. Conditional distribution provides ...

In the field of statistics and probability, marginal distribution plays a critical role in understanding the behavior of variables. It is a method used to determine the probability distribution of a ...