def test_bpic():
"""Test Bayesian predictive information criterion"""
x_obs = np.arange(6)
with pm.Model() as model:
p = pm.Beta('p', 1., 1., transform=None)
x = pm.Binomial('x', 5, p, observed=x_obs)
step = pm.Metropolis()
trace = pm.sample(100, step)
calculated = pm.bpic(trace)
mean_deviance = -2 * st.binom.logpmf(np.repeat(np.atleast_2d(x_obs), 100, axis=0), 5,
np.repeat(np.atleast_2d(trace['p']), 6, axis=0).T).sum(axis=1).mean()
deviance_at_mean = -2 * st.binom.logpmf(x_obs, 5, trace['p'].mean()).sum()
actual = 3 * mean_deviance - 2 * deviance_at_mean
assert_almost_equal(calculated, actual, decimal=2)
def test_bpic():
"""Test Bayesian predictive information criterion"""
x_obs = np.arange(6)
with pm.Model() as model:
p = pm.Beta('p', 1., 1., transform=None)
x = pm.Binomial('x', 5, p, observed=x_obs)
step = pm.Metropolis()
trace = pm.sample(100, step)
calculated = pm.bpic(trace)
mean_deviance = -2 * st.binom.logpmf(np.repeat(np.atleast_2d(x_obs), 100, axis=0), 5,
np.repeat(np.atleast_2d(trace['p']), 6, axis=0).T).sum(axis=1).mean()
deviance_at_mean = -2 * st.binom.logpmf(x_obs, 5, trace['p'].mean()).sum()
actual = 3 * mean_deviance - 2 * deviance_at_mean
assert_almost_equal(calculated, actual, decimal=2)
