Explaining Away in Weight Space
Peter Dayan   Sham Kakade
NIPS 2000, 451-457.
Abstract
Explaining away has mostly been considered in terms of inference of
states in belief networks. We show how it can also arise in a Bayesian
context in inference about the weights governing relationships such as
those between stimuli and reinforcers in conditioning experiments such
as backward blocking. We show how explaining away in weight
space can be accounted for using an extension of a Kalman filter
model; provide a new approximate way of looking at the Kalman gain
matrix as a whitener for the correlation matrix of the observation
process; suggest a network implementation of this whitener using an
architecture due to Goodall; and show that the resulting model
exhibits backward blocking.
compressed postscript