def diagnostics(self):
if self._diagnostics:
return self._diagnostics
for site in self.sites:
site_stats = OrderedDict()
try:
site_stats["n_eff"] = stats.effective_sample_size(
self.support()[site])
except NotImplementedError:
site_stats["n_eff"] = torch.tensor(float('nan'))
site_stats["r_hat"] = stats.split_gelman_rubin(
self.support()[site])
self._diagnostics[site] = site_stats
return self._diagnostics
def test_effective_sample_size():
x = torch.arange(1000.0).reshape(100, 10)
with xfail_if_not_implemented():
# test against arviz
assert_equal(effective_sample_size(x).item(), 52.64, prec=0.01)