GaussianCopula.cross_entropy()

GaussianCopula.cross_entropy(other, name='cross_entropy')

Computes the (Shannon) cross entropy.

Denote this distribution (self) by P and the other distribution by Q. Assuming P, Q are absolutely continuous with respect to one another and permit densities p(x) dr(x) and q(x) dr(x), (Shannon) cross entropy is defined as:

`none H[P, Q] = E_p[-log q(X)] = -int_F p(x) log q(x) dr(x) `

where F denotes the support of the random variable X ~ P.

Parameters:

• other – tfp.distributions.Distribution instance.

• name (default: 'cross_entropy') – Python str prepended to names of ops created by this function.

Returns:

cross_entropy –

self.dtype Tensor with shape [B1, …, Bn]

representing n different calculations of (Shannon) cross entropy.