Causal Independence for Knowledge Acquisition and Inference
  I introduce a temporal belief-network representation of causal independence
that a knowledge engineer can use to elicit probabilistic models. Like the
current, atemporal belief-network representation of causal independence, the
new representation makes knowledge acquisition tractable. Unlike the atemproal
representation, however, the temporal representation can simplify inference,
and does not require the use of unobservable variables. The representation is
less general than is the atemporal representation, but appears to be useful for
many practical applications.
