My real problem has a much more complexity and a different function than following. However, for the sake of simplicity assume I have a data that can be described as a one dimensional Gaussian function:
$$y = Ae\left(\frac{-(x-\mu)^2}{\sigma}\right)$$
The $x$ variable which is the input to this Gaussian function is not an independent variable, rather $x$ values could be time for example. The output of the function is only the intensity of a signal.
After I fit my Gaussian function to my data I will have three fitted parameters $A$, $\mu$, and $\sigma$.
If the problem was regression, that is having an independent variable $x$ and the dependent variable $y$, then I would use the covariance matrix.
However, in this case of curve fitting, $x$ is not an independent variable.
How can I estimate the uncertainty in the model fits?
Some references to learn about uncertainty in curve fitting is also appreciated.