def test_fitting_beta_binomial():
print("\nTesting Beta-Binomial with n=100, a=2, b=3, loc=1, scale=2:")
dist = RVGs.BetaBinomial(n=20, a=2, b=3, loc=1)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
# method of moment
dict_mm_results = RVGs.BetaBinomial.fit_mm(mean=np.mean(data),
st_dev=np.std(data),
n=20,
fixed_location=1)
# maximum likelihood
dict_ml_results = RVGs.BetaBinomial.fit_ml(data=data, fixed_location=1)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_beta_binomial_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_beta_binomial_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
def test_beta_binomial(rnd, n, a, b, loc=0):
# beta random variate generator
beta_binomial_dist = RVGs.BetaBinomial(n, a, b, loc)
# obtain samples
samples = get_samples(beta_binomial_dist, rnd)
# report mean and variance
print_test_results('BetaBinomial',
samples,
expectation=(a * n / (a + b)) + loc,
variance=(n * a * b * (a + b + n)) / ((a + b)**2 *
(a + b + 1)))