I am trying to fit experimental data of the PL blinking of the individual quantum dots. They are known to switch their state with some probabilty that is changing depending on their previous state. To me it is easier to describe this process by generating the large dataset of random numbers and to look how does the histogram of such distribution fits the real data.
I narrowed the question to fitting distribution of random numbers by another set of random numbers. However, this approach always generates answer
"Optimization completed because the size of the gradient is less than the default value of the optimality tolerance."
"Options relative first-order optimality = 0.00e+00 OptimalityTolerance = 1e-06 (default)"
I tried to track the call to the blink function and I saw that it is always called with initial values only.
Please see the code attached:
fitPar1 = lsqcurvefit(F1,Par0,xi,yi)
fitPar2 = lsqcurvefit(F2,Par0,xi,yi)