When dealing with any type of dataset it is important to create a probabilistic model of the data which would help to understand the dataset. The probabilistic model uses probability distribution which assists in explaining about dataset with the implementation of random processes. The main reason for this blog is that in recent years there has been an increase in the use of probability distributions in different disciplines like engineering, biology, weather forecasting, stocks and investments and many more. Hence, it is very important to fit the theoretical distribution to a custom dataset to find the distribution that could provide information/knowledge about the input data.
