The Rescorla-Wagner Algorithm and Maximum Likelihood Estimation of Causal Parameters

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Alan L. Yuille


This paper analyzes generalization of the classic Rescorla-Wagner (R- W) learning algorithm and studies their relationship to Maximum Like- lihood 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 gener- icity 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 conver- gence rates for the GLRW algorithms under these conditions.