Independence gives the joint probability density function on . The inverse transformation is
Its Jacobian matrix has absolute determinant . One way to see the determinant is to use polar coordinates: , , with . Then . The change-of-variables formula for probability densities gives
and zero outside that support. Integrating over the complementary variable gives
These are respectively a unit-rate exponential distribution and a unit-scale half-Cauchy distribution. Their product equals the joint probability density function, so and are independent. This radial-ratio independence of two half-normal variables results from the radial symmetry of the positive-quadrant joint probability density function.
Set . In polar coordinates, the event is , the angular sector
Because has the continuous uniform distribution on an interval of length , the probability is the sector's angular length divided by :
The symmetry of the standard normal distribution in makes the lower sector equally likely. Their disjoint union is , apart from , an event of probability zero. Therefore the requested cumulative distribution function is
This is the unit-scale half-Cauchy distribution. At there is no atom, and as the cumulative distribution function tends to one.
Figure 1.
Gaussian angular probability and the support of a transformed exponential pair
. Details.
The left panel uses , giving a sector of angle and probability . The right panel displays the domain for the next change of variables for probability densities; a vertical section through it determines the conditional range.
Radial-ratio independence of two half-normal variables Created 2026-10-14 Updated 2026-11-05
If are independent with density on the positive axis, the Jacobian matrix of the inverse transformation has determinant . The joint density is for , factoring into an exponential distribution for and a unit-scale half-Cauchy distribution for . The underlying radial symmetry makes radius and angle independent.