def check_prior_support(prior):
"""Check whether prior allows to check for support.
This either uses the PyTorch support property, or the custom prior .logprob method
"""
try:
within_support(prior, prior.sample())
except NotImplementedError:
raise NotImplementedError(
"""The prior must implement the support property or allow to call
.log_prob() outside of support.""")
def __call__(self, theta: Tensor, track_gradients: bool = True) -> Tensor:
r"""Returns the potential for posterior-based methods.
Args:
theta: The parameter set at which to evaluate the potential function.
track_gradients: Whether to track the gradients.
Returns:
The potential.
"""
theta = ensure_theta_batched(torch.as_tensor(theta))
theta, x_repeated = match_theta_and_x_batch_shapes(theta, self.x_o)
theta, x_repeated = theta.to(self.device), x_repeated.to(self.device)
with torch.set_grad_enabled(track_gradients):
posterior_log_prob = self.posterior_estimator.log_prob(
theta, context=x_repeated)
# Force probability to be zero outside prior support.
in_prior_support = within_support(self.prior, theta)
posterior_log_prob = torch.where(
in_prior_support,
posterior_log_prob,
torch.tensor(float("-inf"),
dtype=torch.float32,
device=self.device),
)
return posterior_log_prob