Fitting

To fit the distribution to data, the syntax is simply distribution.fit(data), where fixed parameters can be added as additional arguments to the fit, as in the package scipy.stats.

Let’s generate a larger sample from our previous object:

>>> data = Normal(1, 2).rvs(1000)
>>> data.mean()
1.025039359276458
>>> data.std()
1.9376460645596842

We can fit a Normal distribution to this data, which will return another Normal object:

>>> Normal().fit(data)
Normal(loc=1.0250822420920338, scale=1.9376400770300832)

As you can see, the values are slightly different from the moments in the data. This is due to the fact that the fit method returns the Maximum Likelihood Estimator (MLE) for the data, and is thus the result of an optimisation (using scipy.optimize).

The syntax distribution.fit(data, loc=1) can be used to fit the distribution to the data while keeping the loc parameter at a fixed value, in this case 1:

>>> Normal().fit(data, loc=1)
Normal(loc=1.0, scale=1.9377929687500024)