BEST test

Description

Perform the Bayesian test for difference of means and standard deviations between parameters as described in the classic BEST paper (Kruschke, 2012). The prior distributions used are the same as in the paper (Student's T for data, normal for means, uniform for standard deviations, shifted exponential for normality)

Example usage

import numpy as np
import matplotlib.pyplot as plt
from best_test import BayesianDifferenceTest

observed_a = np.random.random(100)
observed_b = np.random.random(85) # unequal sample sizes are not an issue

mu_mean = 0 # mean for the prior on mean
mu_sd = 1 # standard deviation on the prior on mean
sd_lower = .1 # lower bound for the prior on standard deviation
sd_upper = 10 # upper bound for the prior on standard deviation
nu_mean = 30 # mean on the prior on nu (normality, aka 'degrees of freedom')

test = BayesianDifferenceTest('param_a_name', 'param_b_name',
                               mu_mean, mu_sd, 
                               sd_lower, sd_upper,
                               nu_mean)
test.run(observed_a, observed_b)
test.plot_posterior(ref_val=0)
plt.show()

Requirements

• Python >= 3.4
• pymc3 >= 3.1

Notes

This program was written as a part of my internship in MAASTRO Clinic Knowledge Engineering team. It is available under the MIT licence.

References

• Bayesian estimation supersedes the t test. Kruschke JK. J Exp Psychol Gen. 2013 May;142(2):573-603. doi: 10.1037/a0029146. Epub 2012 Jul 9.