  To date, most probabilistic reasoning systems have relied on a fixed belief
network constructed at design time. The network is used by an application
program as a representation of (in)dependencies in the domain. Probabilistic
inference algorithms operate over the network to answer queries. Recognizing
the inflexibility of fixed models has led researchers to develop automated
network construction procedures that use an expressive knowledge base to
generate a network that can answer a query. Although more flexible than fixed
model approaches, these construction procedures separate construction and
evaluation into distinct phases. In this paper we develop an approach to
combining incremental construction and evaluation of a partial probability
model. The combined method holds promise for improved methods for control of
model construction based on a trade-off between fidelity of results and cost of
construction.
