Inference in a DAG 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 so multiplying v0 by W transpose gives the product of the likelihood term and the prior term. • Inference in the DAG is exactly equivalent to letting a Restricted Boltzmann Machine settle to equilibrium starting at the data.

v2

h1

+

+

h0

+

+

v0