The Rescorla-Wagner Algorithm and Maximum Likelihood Estimation of Causal Parameters
Alan Yuille · eScholarship (California Digital Library) · 2004
This paper analyzes generalization of the classic Rescorla-Wagner (R-W) learning algorithm and studies their relationship to Maximum Likelihood estimation of causal parameters.We prove that the parameters of two popular causal models, ∆P and P C, can be learnt by the same generalized linear Rescorla-Wagner (GLRW) algorithm provided genericity conditions apply.We characterize the fixed points of these GLRW algorithms and calculate the fluctuations about them, assuming that the input is a set of i.i.d.samples from a fixed (unknown) distribution.We describe how to determine convergence conditions and calculate convergence rates for the GLRW algorithms under these conditions.