Relationship between dist. of x and dist. of 1/x?

  • Thread starter Thread starter Mapes
  • Start date Start date
  • Tags Tags
    Relationship
AI Thread Summary
The discussion focuses on the relationship between the distribution of stiffness measurements and the distribution of compliance values, defined as the reciprocal of stiffness. It highlights that while there is no universal statement regarding the relationship between the distributions of x and 1/x, specific cases can be analyzed using probability conservation arguments. The transformation of distributions can be expressed mathematically, where the distribution g(y) of compliance can be derived from the distribution f(x) of stiffness. However, the nature of the resulting distribution for 1/x depends on the original distribution of x, making it complex and case-specific. Overall, understanding these transformations requires careful consideration of the underlying distributions involved.
Mapes
Science Advisor
Homework Helper
Gold Member
Messages
2,591
Reaction score
21
I've been working recently in the area of tissue cell mechanics; specifically, I'm measuring mechanical stiffness (or compliance, the reciprocal of stiffness) and considering its possible underlying distribution.

I was wondering about the following: If the distribution of stiffness measurements is approximately Gaussian (or lognormal, or gamma distributed, etc.), then what can we say about the distribution of the corresponding compliance (= 1/stiffness) values? More generally, if x is distributed in a certain way, what about 1/x? Is there a simple relationship?
 
Physics news on Phys.org
No - there is no universal statement that can be made. Each case needs to be considered on its own.
(The mathematical ideas behind studying the distributions is the same in each case, but unless I'm totally off that wasn't the point of your inquiry.)
 
Mapes said:
More generally, if x is distributed in a certain way, what about 1/x? Is there a simple relationship?

There is a relationship, how simple depends on the details of your example.

Given a probability distribution f(x), we seek the distribution g(y) where y is a function of x. A simple probability conservation argument tells us that

f(x) |dx| = g(y) |dy|​

so that

g(y) = f(x) / |dy/dx|​

Take y = 1/x, and f(x) is whatever you think, you can get g(y).


EDIT:
Continuing the example for y = 1/x

Since |dy/dx| = 1/x2 = y2, we have

g(y) = f(x) / y2

And, of course, you would substitute 1/y for x in the expression for f(x).
 
Last edited:
The transformation approach is correct (modulo being careful around x = 0); my intention was to say there is nothing simple to say about the type of distribution for X and the type for 1/X (normal to normal, t to t, etc).
 
Back
Top