ab_test_dist_explorer.md

Class ab_test_dist_explorer

This file contains a set of functions to help a user visualize each distribution.

Methods

• beta_dist(alpha, beta)
  • requires - alpha and beta are non-negative integers. alpha signifies the successes, and beta signifies the failures. ex: alpha = 10, beta = 10. Sucess rate = 50% out of a population of 20.
  • modifies - returns a matplot lib object (show the plot with matplotlib.pyplot.show())
  • effects - none
• gamma_dist(mean=None, var=None, alpha=None, beta=None)
  • requires - Inputs are in pairs, either [mean, variance] OR [alpha, beta]. Mean is on the inveval (-inf, inf), variance is greater than 0. Alpha and beta are both postive numbers.
  • modifies - returns a matplot lib object (show the plot with matplotlib.pyplot.show())
  • effects - none
• lognormal_dist(mean, var)
  • requires - mean and variance of the log-normal distribution. Mean is on the inveval (-inf, inf), variance is greater than 0.
  • modifies - returns a matplot lib object (show the plot with matplotlib.pyplot.show())
  • effects - none
• poisson_dist(lam)
  • requires - lambda is the average occurences per unit time in a poisson distribution. Lambda is non-negative.
  • modifies - returns a matplot lib object (show the plot with matplotlib.pyplot.show())
  • effects - none

Usage Guide

# import packages
from BayesABTest import ab_test_dist_explorer as pe
from matplotlib import pyplot as plt

# use the functions to plot a distribution
pe.beta_dist(20, 80)
plt.show()

Examples

Beta(20, 80) Distribution

Gamma(4, 2) Distribution

Poisson(15) Distribution