Aim : To fit a known probability distribution to a particular dataset based on Pearson’s Distributions. The sample data can be in different forms, for example, they can be a series of isolated points (single data points) or binned data represented by upper and lower bounds.
A conventional way to solve the problem of data fitting is by the least square approximation method. But here, we will use Pearson’s method and apply the theory, which uses a general differential equation to represent different types of distributions. We use method of moments to know about the inferences we can make using the sample data and classify the observed data into certain types which would in turn tell us the curve which would be a reasonably good fit for the above mentioned dataset.
For a detailed description: https://github.com/anshu1997/Pearson-s-Curves/blob/master/Summer_Project_Report.pdf
anshumanchak/Pearson-s-Curves
Fitting a known probability distribution to a particular dataset based on Pearson’s Distributions.
Python