  We propose using recognition networks for approximate inference inBayesian
networks (BNs). A recognition network is a multilayerperception (MLP) trained
to predict posterior marginals given observedevidence in a particular BN. The
input to the MLP is a vector of thestates of the evidential nodes. The activity
of an output unit isinterpreted as a prediction of the posterior marginal of
thecorresponding variable. The MLP is trained using samples generated fromthe
corresponding BN.We evaluate a recognition network that was trained to do
inference ina large Bayesian network, similar in structure and complexity to
theQuick Medical Reference, Decision Theoretic (QMR-DT). Our networkis a
binary, two-layer, noisy-OR network containing over 4000 potentially observable
nodes and over 600 unobservable, hidden nodes. Inreal medical diagnosis, most
observables are unavailable, and there isa complex and unknown bias that
selects which ones are provided. Weincorporate a very basic type of selection
bias in our network: a knownpreference that available observables are positive
rather than negative.Even this simple bias has a significant effect on the
posterior. We compare the performance of our recognition network
tostate-of-the-art approximate inference algorithms on a large set oftest
cases. In order to evaluate the effect of our simplistic modelof the selection
bias, we evaluate algorithms using a variety ofincorrectly modeled observation
biases. Recognition networks performwell using both correct and incorrect
observation biases.
