< Probability distributions list < Continuous joint distribution
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 . The leap from discrete joint distributions to continuous ones is much like the leap from single variable discrete random variable to continuous ones. However, as continuous joint distributions are two dimensional, double integrals (from calculus) 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 , calculate by a double integral ∫∫.
Two random variables X and Y are jointly continuous if there exists an integrable non-negative function fXY: ℝ2 → ℝ such that for any set A ∈ ℝ
Example 1: Given the following joint PDF, find the constant c:
 Liu, M. STA 611: Introduction to Mathematical Statistics; Lecture 4: Random Variables and Distributions. Retrieved November 10, 2021 from: http://www2.stat.duke.edu/courses/Fall18/sta611.01/Lecture/Lecture04.pdf
 Westfall, P. & Henning, K. (2013). Understanding Advanced Statistical Methods. CRC Press.