probability distribution

These days, I often use probability distribution, usualy shorten as PD, to asume a sequence of data following some model.

There are various kind of PD, especially the normalized distribution is the most likely used one.

I am now researching about NIG, normal inverse gaussian, distribution. This is an extended model of normalized distribution.

The difference between two PDs is that the shape can be transformed with its parameters, not only symmetric but assimmetric shape, also light tail to fat tail. The adoptability makes this to be used model financial data, especially market data.