Uncertainty Propagation Argument

In summary, the conversation discusses the derivation of the uncertainty in a function or quantity F, which is given by a summation of the partial derivatives of F with respect to each variable, multiplied by the uncertainty in each variable. The argument for this derivation involves thinking of the variables as perpendicular vectors in a right triangle, and combining their uncertainties in quadrature. However, there may be other methods for computing a margin of error for F depending on how the variables are related.
  • #1
azaharak
152
0
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


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


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

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

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

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


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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  • #2
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:
  • #3
Anyone?
 
  • #4
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
 
  • #5
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.
 

Related to Uncertainty Propagation Argument

1. What is uncertainty propagation argument?

Uncertainty propagation argument is a mathematical method used to quantify the uncertainty in a measurement or calculation based on the uncertainty in the input parameters or variables. It is commonly used in fields such as physics, engineering, and statistics.

2. How is uncertainty propagation argument used in scientific research?

Uncertainty propagation argument is used to assess the reliability and accuracy of experimental results, as well as to make predictions and evaluate the significance of data. It is also used to determine the impact of uncertainties on the overall conclusions of a study.

3. What are the key assumptions made in uncertainty propagation argument?

The key assumptions made in uncertainty propagation argument include the assumption of linearity (that the relationship between input and output variables is linear), the assumption of independence (that uncertainties in different variables are not correlated), and the assumption of normality (that uncertainties follow a normal distribution).

4. How is uncertainty propagated through a calculation or measurement?

Uncertainty propagation involves using mathematical equations, such as the law of propagation of uncertainty, to calculate the uncertainty in the final output based on the uncertainties in the input variables. This can be done using methods such as the Monte Carlo simulation or the Taylor series expansion.

5. What are some limitations of uncertainty propagation argument?

One limitation of uncertainty propagation argument is that it assumes that all uncertainties are known and can be quantified accurately. In reality, uncertainties may be difficult to measure or may be affected by external factors. Additionally, this method may not work well for non-linear relationships between variables or in cases where uncertainties are highly correlated.

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