Bo Thiesson and Christopher Meek
We introduce Recursive Exponential Mixed Models (REMMs) and derive the gradient of the parameters for the incomplete-data likelihood. We demonstrate how one can use probabilistic inference in Conditional Gaussian (CG) graphical models, a special case of REMMs, to compute the gradient for a CG model. We also demonstrate that this approach can yield simple and effective algorithms for computing the gradient for models with tied parameters and illustrate this approach on stochastic ARMA models.
|Published in||Proceedings of Tenth International Workshop on Artificial Intelligence and Statistics|
|Publisher||The Society for Artificial Intelligence and Statistics|
Copyright © 2005 by The Society for Artificial Intelligence and Statistics.