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In the single-variable case, the condition that there are no point mass concentrations on the line ensures the existence of a probability density function, useful in probability calculations.A similar situation exists for a joint distribution for two (or more) variables. For any joint mapping to the plane which assigns zero probability to eachset with zero area (discrete points, line or curve segments, and countable unions of these) there is a density function.
Definition
If the joint probability distribution for the pair assigns zero probability to every set of points with zero area, then there exists a joint density function with the property
We have three properties analogous to those for the single-variable case:
At every continuity point for , the density is the second partial
Now
A similar expression holds for . Use of the fundamental theorem of calculus to obtain the derivatives gives the result
Marginal densities . Thus, to obtain the marginal density for the first variable, integrate out the second variablein the joint density, and similarly for the marginal for the second variable.
Let . This region is the triangle bounded by , , and (see [link] )
where Q is the common part of the triangle with the strip between and and above the line . This is the small triangle bounded by , , and . Thus
The pair has joint density on the region bounded by , , , and (see [link] ). Determine the marginal density f X .
SOLUTION
Examination of the figure shows that we have different limits for the integral with respect to u for and for .
We may combine these into a single expression in a manner used extensively in subsequent treatments. Suppose and . Then for (i.e., ) and zero elsewhere. Likewise, for and zero elsewhere. We can, therefore express f X by
For a pair with joint density , we approximate the distribution in a manner similar to that for a single random variable. We then utilize the techniquesdeveloped for a pair of simple random variables. If we have n approximating values t i for X and m approximating values u j for Y , we then have pairs , corresponding to points on the plane. If we subdivide the horizontal axis for values of X , with constant increments , as in the single-variable case, and the vertical axis for values of Y , with constant increments , we have a grid structure consisting of rectangles of size . We select t i and u j at the midpoint of its increment, so that the point is at the midpoint of the rectangle. If we let the approximating pair be , we assign
As in the one-variable case, if the increments are small enough,
The m-procedure tuappr calls for endpoints of intervals which include the ranges of X and Y and for the numbers of subintervals on each. It then prompts for an expression for , from which it determines the joint probability distribution. It calculates the marginal approximatedistributions and sets up the calculating matrices t and u as does the m-process jcalc for simple random variables. Calculations are then carried outas for any joint simple pair.
Determine .
>> tuappr
Enter matrix [a b] of X-range endpoints [0 1]
Enter matrix [c d] of Y-range endpoints [0 1]
Enter number of X approximation points 200Enter number of Y approximation points 200
Enter expression for joint density 3*(u <= t.^2)
Use array operations on X, Y, PX, PY, t, u, and P>> M = (t <= 0.8)&(u > 0.1);>> p = total(M.*P) % Evaluation of the integral with
p = 0.3355 % Maple gives 0.3352455531 The discrete approximation may be used to obtain approximate plots of marginal distribution and density functions.
on the triangle bounded by , , and .
>> tuappr
Enter matrix [a b] of X-range endpoints [-1 1]
Enter matrix [c d] of Y-range endpoints [0 1]
Enter number of X approximation points 400Enter number of Y approximation points 200
Enter expression for joint density 3*u.*(u<=min(1+t,1-t))
Use array operations on X, Y, PX, PY, t, u, and P>> fx = PX/dx; % Density for X (see
[link] )
% Theoretical (3/2)(1 - |t|)^2>> fy = PY/dy; % Density for Y>> FX = cumsum(PX); % Distribution function for X (
[link] )>> FY = cumsum(PY); % Distribution function for Y>> plot(X,fx,X,FX) % Plotting details omitted These approximation techniques useful in dealing with functions of random variables, expectations, and conditional expectation and regression.
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