Why do Jacobian transformations in probability densities require a reciprocal?

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SUMMARY

The discussion centers on the necessity of including a reciprocal of the Jacobian determinant in the transformation of probability densities. Specifically, when transforming from variables (X, Y) to (U, V) using functions g and h, the relationship is defined as f_{U,V}(u,v) du dv = f_{X,Y}(h_1(u,v), h_2(u,v)) |J(h_1(u,v), h_2(u,v))|^{-1} dx dy. This contrasts with standard calculus transformations where the Jacobian determinant is used without a reciprocal. The inclusion of the reciprocal in probability density transformations ensures proper normalization of the probability measure.

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  • Basic concepts of integration and substitution methods
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IniquiTrance
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Why is it that if you have:

[tex]U=g_1 (x, y), \quad V = g_2 (x,y)[/tex]
[tex]X = h_1 (u,v), \quad Y = h_2 (u,v)[/tex]

Then:

[tex]f_{U,V} (u,v) du dv = f_{X,Y} (h_1(u,v), h_2 (u,v)) \left|J(h_1(u,v),h_2(u,v))\right|^{-1} dxdy[/tex]

While when doing variable transformations in calculus, you have:

[tex]du dv = \left|J(h_1(u,v),h_2(u,v))\right| dx dy[/tex]

without the reciprocal. Why is it that with the probability densities, you take the reciprocal, rather than how it's typically done without the reciprocal?

Thanks!
 
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IniquiTrance said:
While when doing variable transformations in calculus, you have:
[tex]du dv = \left|J(h_1(u,v),h_2(u,v))\right| dx dy[/tex]We should check that.

We could begin by looking at a simpler case.

If we consider the integral [itex]\int_{0}^{1} 1 du[/itex] and used the substitution [itex]x = 2u[/itex], we have [itex]du = (1/2) dx[/itex] and the range of [itex]x[/itex] in the integration is [0,2].

As I relate this to the notation in your question, [itex]x = h_1(u) = 2u[/itex].
[itex]| J(h_1(u)]| = 2[/itex].
 
Thank you.
 

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