# Introduction to Common Distributions¶

## Parametric vs. Non-parametric Models¶

Parametric methods assume that the data can be represented using a model with a finite number of parameters. The number of parameters is bounded and as a result is inflexible for modeling data when the size of the data grows. In non-parametric methods we do not model the data with a preset number of parameters. The number of parameters here grow with the size of the data and as a result can be quite flexible in modeling complex phenomena.

## Parametric Methods¶

from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
import numpy as np
import scipy
from scipy.special import gamma, factorial, comb
import plotly.express as px
import plotly.offline as pyo
import plotly.graph_objs as go
# Set notebook mode to work in offline
pyo.init_notebook_mode()