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)))