Continuous Joint Distribution

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A continuous joint distribution describes the probability of interaction between two continuous random variables. Its discrete counterpart is the discrete joint distribution which has a countable number of possible outcomes (e.g., 1, 2, 3…).

Continuous joint distributions can be described by a non-negative, integrable function [1]. The leap from discrete joint distributions to continuous ones is much like the leap from single variable discrete random variables to continuous ones. However, as continuous joint distributions are two dimensional, double integrals are needed instead of sigma notation (Σ) to solve probability problems.

The continuous joint distribution PDF

The continuous joint distribution assigns relative likelihoods, via the likelihood function, to combinations of x and y. The probabilities p(x, y) are not traditional probabilities, in the sense that you can get a probability of, say 99% or 50% or 10%; as this is a continuous distribution, the probability of a specific value is always zero. Probabilities for joint continuous distributions are instead treated as volume problems [2], calculate with a double integral ∫∫.

Continuous joint distributions are formally described by a joint probability density function, much in the same way that random variables are described by a “single” probability density function (pdf) , defined as follows:

Two random variables X and Y are jointly continuous if there exists an integrable, non-negative function (a joint pdf) f_XY: ℝ² → ℝ such that for any set A ∈ ℝ

continuous joint distribution density function definition