Inference in a directed net
with replicated weights
etc.
h2
The variables in h0 are conditionally
independent given v0.
Inference is trivial. We just
multiply v0 by W transpose.
The model above h0 implements
a complementary prior.
Multiplying v0 by W transpose
gives the product of the likelihood
term and the prior term.
Inference in the directed net is
exactly equivalent to letting a
Restricted Boltzmann Machine
settle to equilibrium starting at the
data.
v2
h1
+
+
h0
+
+
v0