Explaining Away in Weight Space

10 years 3 months ago
Explaining Away in Weight Space
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.
Peter Dayan, Sham Kakade
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where NIPS
Authors Peter Dayan, Sham Kakade
Comments (0)