- #1
RaamGeneral
- 50
- 1
Hi.
Suppose I have a function [itex]y=f(a,b;p;x)[/itex] where a, b are known with some uncertainty and x, y were measured multiple times.
I want to find p through best fit.
I consider a, b to be gaussian variable.
I can imagine multiple ways to do this:
* I consider a, b parameters like p. I don't like this, moreover with the function I'm working on, I get uncertainties orders of magnitude greater than the value of the parameters.
* I ignore the uncertainties for a, b and proceed to do a numerical fit for parameter p like I would normally do.
* I thought I could make a large number of fits where a, b get gaussian-random values and obtain the probability distribution of p (I ignore the std dev given by the fits). Supposing this makes sense, how do I get the uncertainty for p from his probability distribution if this is not a gaussian function?
The function I'm working on is [tex] v(t) = \frac{F_T}{ \lambda} \ln \left( \frac{M_0 }{M_0 - \lambda t } \right)- gt [/tex] where [itex]F_T[/itex] is known exactly and [itex]M_0[/itex] is the parameter.
[itex]\lambda[/itex] and [itex]g[/itex] are known with uncertainties.Thank you very much for any advice.
I have another problem, again with uncertainties, that arises from the same exercise. I will make another post in the future about that.
Suppose I have a function [itex]y=f(a,b;p;x)[/itex] where a, b are known with some uncertainty and x, y were measured multiple times.
I want to find p through best fit.
I consider a, b to be gaussian variable.
I can imagine multiple ways to do this:
* I consider a, b parameters like p. I don't like this, moreover with the function I'm working on, I get uncertainties orders of magnitude greater than the value of the parameters.
* I ignore the uncertainties for a, b and proceed to do a numerical fit for parameter p like I would normally do.
* I thought I could make a large number of fits where a, b get gaussian-random values and obtain the probability distribution of p (I ignore the std dev given by the fits). Supposing this makes sense, how do I get the uncertainty for p from his probability distribution if this is not a gaussian function?
The function I'm working on is [tex] v(t) = \frac{F_T}{ \lambda} \ln \left( \frac{M_0 }{M_0 - \lambda t } \right)- gt [/tex] where [itex]F_T[/itex] is known exactly and [itex]M_0[/itex] is the parameter.
[itex]\lambda[/itex] and [itex]g[/itex] are known with uncertainties.Thank you very much for any advice.
I have another problem, again with uncertainties, that arises from the same exercise. I will make another post in the future about that.