def _log_normalizer(self):
return torch.lgamma(.5 * self.df) - torch.lgamma(.5 * (self.df + self.loc.shape[-1])) \
+ .5 * torch.log(math.pi * self.df) + triangular_logdet(self.scale_tril)
def entropy(self) -> torch.Tensor:
logdet = triangular_logdet(self.scale_tril)
return .5 * self.event_shape[0] * (1. + _LOG_2PI) + logdet
def log_normalizer(self):
D = self.event_shape[-1]
return torch.mvlgamma(self.concentration, D) + self.concentration \
* (D * math.log(2.) + 2. * util.triangular_logdet(self.scale_tril))
def _log_normalizer(self, nat_param1, nat_param2):
maha, prec_tril = mahalanobis(-2. * nat_param2, nat_param1)
logdet = triangular_logdet(prec_tril)
return .5 * maha - logdet + .5 * self.event_shape[0] * _LOG_2PI
def expected_logdet(self):
D = self.event_shape[-1]
return mvdigamma(self.concentration, D) + D * math.log(2.) \
+ 2. * util.triangular_logdet(self.scale_tril)