Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Exponential family predictive representations of state
Exponential family predictive representations of state
ECML'05 Proceedings of the 16th European conference on Machine Learning
Closing the learning-planning loop with predictive state representations
International Journal of Robotics Research
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Exponential Family PSR (EFPSR) models capture stochastic dynamical systems by representing state as the parameters of an exponential family distribution over a shortterm window of future observations. They are appealing from a learning perspective because they are fully observed (meaning expressions for maximum likelihood do not involve hidden quantities), but are still expressive enough to both capture existing models and predict new models. While maximum-likelihood learning algorithms for EFPSRs exist, they are not computationally feasible. We present a new, computationally efficient, learning algorithm based on an approximate likelihood function. The algorithm can be interpreted as attempting to induce stationary distributions of observations, features and states which match their empirically observed counterparts. The approximate likelihood, and the idea of matching stationary distributions, may apply to other models.