Performance Guarantees for Information Theoretic Active Inference
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:620-627, 2007.
In many estimation problems, the measurement process can be actively controlled to alter the information received. The control choices made in turn determine the performance that is possible in the underlying inference task. In this paper, we discuss performance guarantees for heuristic algorithms for adaptive measurement selection in sequential estimation problems, where the inference criterion is mutual information. We also demonstrate the performance of our tighter online computable performance guarantees through computational simulations.