1 Department of Epidemiology, Harvard School of Public Health, Boston, USA 2 Departments of Epidemiology and Biostatistics, Harvard School of Public Health Correspondence to: Dr M A Hernán Department ...

Probability theory is indispensable in computer science: It is at the core of artificial intelligence and machine learning, which require decision making under uncertainty. It is integral to CS theory ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

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 ...

Apply arithmetic mean of frequency distribution to find the expected value of a random variable The expected value of discrete random variable as summation of product of discrete random variable by ...

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 continuous random variable is a type of variable that can take on any value within a given range. Unlike discrete random variables, which have a countable number of outcomes, continuous random ...

AIFAD stands for Automated Induction of Functions over Algebraic Data Types and is an application written in OCaml that improves decision tree learning by supporting significantly more complex kinds ...

Probability Distribution Notes: Probability is a fundamental aspect of mathematics that helps us understand and quantify uncertainty. Mastery of this subject is essential for students, as it has ...

dxxx(x,) returns the density or the value on the y-axis of a probability distribution for a discrete value of x pxxx(q,) returns the cumulative density function (CDF) or the area under the curve to ...

Background: Although several strategies for modelling competing events in discrete event simulation (DES) exist, a methodological gap for the event-specific probabilities and distributions (ESPD) ...