Maximum Likelihood Estimation (MLE) with optimizer

SimpleMC through the MaxLikeAnalyzer class uses the L-BFGS-B algorithm from scipy.optimize.minimize and tries to maximize the Likelihood function based on a cosmological model and selected datasets. It then obtains the errors of the Hessian.

An example of ini file to use the MaxLikeAnalyzer class is as follows:

[custom]
...

model = LCDM

datasets = SN+HD

analyzer = maxlike
...

[maxlike]
;compute errror from Hessian matrix
;False/True
compute_errors = True

;If withErrors is True
;plot Fisher matrix
show_contours = True

;If showplot is True, then
;2D plot for the parameters:
plot_par1 = h
plot_par2 = Om

;[DerivedParameters]
compute_derived = True

Lastly, we can run SimpleMC as in the example Python script using the ini file with the maxlike information.

Notebook example

In the following notebook there is an example of the use of ga_deap and maxlike.