def _build_prior(self, name, X, reparameterize=True, jitter=JITTER_DEFAULT, **kwargs):
mu = self.mean_func(X)
cov = stabilize(self.cov_func(X), jitter)
if reparameterize:
size = infer_size(X, kwargs.pop("size", None))
v = pm.StudentT(name + "_rotated_", mu=0.0, sigma=1.0, nu=self.nu, size=size, **kwargs)
f = pm.Deterministic(name, mu + cholesky(cov).dot(v))
else:
f = pm.MvStudentT(name, nu=self.nu, mu=mu, cov=cov, **kwargs)
return f
def time_drug_evaluation(self):
# fmt: off
drug = np.array([
101, 100, 102, 104, 102, 97, 105, 105, 98, 101, 100, 123, 105, 103,
100, 95, 102, 106, 109, 102, 82, 102, 100, 102, 102, 101, 102, 102,
103, 103, 97, 97, 103, 101, 97, 104, 96, 103, 124, 101, 101, 100,
101, 101, 104, 100, 101
])
placebo = np.array([
99, 101, 100, 101, 102, 100, 97, 101, 104, 101, 102, 102, 100, 105,
88, 101, 100, 104, 100, 100, 100, 101, 102, 103, 97, 101, 101, 100,
101, 99, 101, 100, 100, 101, 100, 99, 101, 100, 102, 99, 100, 99
])
# fmt: on
y = pd.DataFrame({
"value":
np.r_[drug, placebo],
"group":
np.r_[["drug"] * len(drug), ["placebo"] * len(placebo)],
})
y_mean = y.value.mean()
y_std = y.value.std() * 2
sigma_low = 1
sigma_high = 10
with pm.Model():
group1_mean = pm.Normal("group1_mean", y_mean, sd=y_std)
group2_mean = pm.Normal("group2_mean", y_mean, sd=y_std)
group1_std = pm.Uniform("group1_std",
lower=sigma_low,
upper=sigma_high)
group2_std = pm.Uniform("group2_std",
lower=sigma_low,
upper=sigma_high)
lambda_1 = group1_std**-2
lambda_2 = group2_std**-2
nu = pm.Exponential("ν_minus_one", 1 / 29.0) + 1
pm.StudentT("drug",
nu=nu,
mu=group1_mean,
lam=lambda_1,
observed=drug)
pm.StudentT("placebo",
nu=nu,
mu=group2_mean,
lam=lambda_2,
observed=placebo)
diff_of_means = pm.Deterministic("difference of means",
group1_mean - group2_mean)
pm.Deterministic("difference of stds", group1_std - group2_std)
pm.Deterministic(
"effect size", diff_of_means / np.sqrt(
(group1_std**2 + group2_std**2) / 2))
pm.sample(draws=20000,
cores=4,
chains=4,
progressbar=False,
compute_convergence_checks=False)
def make_model(cls):
with pm.Model() as model:
a = pm.StudentT("a", nu=4, mu=0, sigma=1)
return model