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The Jeffreys Csiszar-function in log-space.
tfp.substrates.jax.vi.jeffreys(
logu, name=None
)
A Csiszar-function is a member of,
F = { f:R_+ to R : f convex }.
The Jeffreys Csiszar-function is:
f(u) = 0.5 ( u log(u) - log(u) )
= 0.5 kl_forward + 0.5 kl_reverse
= symmetrized_csiszar_function(kl_reverse)
= symmetrized_csiszar_function(kl_forward)
This Csiszar-function induces a symmetric f-Divergence, i.e.,
D_f[p, q] = D_f[q, p]
.
Args | |
---|---|
logu
|
float -like Tensor representing log(u) from above.
|
name
|
Python str name prefixed to Ops created by this function.
|
Returns | |
---|---|
jeffreys_of_u
|
float -like Tensor of the Csiszar-function evaluated
at u = exp(logu) .
|