MultivariateNormalDegenerate.kl_divergence()#
- MultivariateNormalDegenerate.kl_divergence(other, name='kl_divergence')#
Computes the Kullback–Leibler divergence.
Denote this distribution (self) by p and the other distribution by q. Assuming p, q are absolutely continuous with respect to reference measure r, the KL divergence is defined as:
```none KL[p, q] = E_p[log(p(X)/q(X))]
= -int_F p(x) log q(x) dr(x) + int_F p(x) log p(x) dr(x) = H[p, q] - H[p]
where F denotes the support of the random variable X ~ p, H[., .] denotes (Shannon) cross entropy, and H[.] denotes (Shannon) entropy.
- Parameters:
other – tfp.distributions.Distribution instance.
name (default:
'kl_divergence') – Python str prepended to names of ops created by this function.
- Returns:
kl_divergence –
- self.dtype Tensor with shape [B1, …, Bn]
representing n different calculations of the Kullback-Leibler divergence.