Uncertainty Propagation Argument

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Discussion Overview

The discussion revolves around the propagation of uncertainty in functions of multiple variables, specifically examining the reasoning behind a formula for calculating uncertainty. Participants explore the validity of the argument presented by a professor regarding the relationship between uncertainties in variables and their geometric interpretation.

Discussion Character

  • Technical explanation, Conceptual clarification, Debate/contested

Main Points Raised

  • One participant presents a formula for uncertainty propagation and relates it to the geometry of a right triangle, suggesting that uncertainties can be treated as perpendicular vectors.
  • Another participant points out a potential error in the notation of the summation in the formula and questions the appropriateness of the context for the question.
  • A different participant introduces an example function to illustrate that the relationship between variables affects how uncertainties combine, suggesting a method of using maximum and minimum values to estimate uncertainty ranges.
  • One participant emphasizes that their inquiry is focused on the validity of the derivation of the uncertainty formula rather than its correctness.

Areas of Agreement / Disagreement

Participants express differing views on the interpretation of the uncertainty propagation argument, with some agreeing on the geometric analogy while others question its applicability. The discussion does not reach a consensus on the validity of the derivation presented.

Contextual Notes

There are unresolved aspects regarding the assumptions made about the relationships between variables and the conditions under which the proposed formula applies. The discussion reflects varying interpretations of how uncertainties should be combined based on different mathematical contexts.

azaharak
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A while back, one of my undergraduate physics professors gave an argument for why the uncertainty in a function or quantity F is given by


\Delta F = \sqrt{^{N}_{i-1}\sum(\frac{\partial F}{\partial x_{i}})^{2}(\Delta x_{i})^{2}}


He argued to think of a right triangle and think of c=\sqrt{a^{2}+b^{2}}

The uncertainty in the length of side c, would be calculate in a similar method, however it would be

\Delta c =\sqrt{(da)^{2}+(db)^{2}} which via chain rule would be

\Delta c =\sqrt{(\frac{\partial c}{\partial a}\Delta a)^{2}+(\frac{\partial c}{\partial b}\Delta b)^{2}}


He then argued that in a function of several variables, those variables can be thought of as perpendicular to each other in the same way that (a) and (b) are in the right triangle (because they pertain to degrees of freedom), this is why we call for the sum in quadrature.


I know that it can be derived from normal distribution, however is the argument above correct reasoning?

Thank you to all

Alex Z
 
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The summation should read (i=1 to i=N) in the first equation.

Maybe this is not the correct place for this question?Looking back at my first post, I think I might have incorrectly worded his arguement.Suppose c is some function of a and b, c is not necessarily the hypotenuse of the triangle. c = f(a,b)

Then because, c has two degrees of freedom, these act like perpendicular vectors (right triangle).

So their uncertainties should combine in quadrature. as I've shown above.

...

It sounds like hand waiving, if that made a sound.
 
Last edited:
Anyone?
 
It's going to depend on how the variables are related. For example, take the function
F = a/b + c
Increasing A or C makes F larger, but in different ways. The only way I know of to compute a margin of error for F is to use maximum and minimum values of a, b, and c and see what range you come up with. Using the above example let's say that
a = 25 +/- 1
b = 5 +/- 1
c = 5 +/- 1

so F could be anywhere between 24/6 + 4 and 26/4 + 6. Anywhere from 8 to 12.5, or 10.25 +/- 2.25
 
I don't think you understand my question.


The formula that I've provided (for the uncertainty in c) is correct, I am not asking if it is. I'm asking about the validity of its derivation by the argument given.
 

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