| In this dissertation, we investigate adaptive strategies for sequential testing, especially those driven by maximizing information gain when the conditional distribution of tests given hypotheses is Gaussian. We implement a classification algorithm in which tests are selected recursively and adaptively on-line. We show that such information-based strategies are statistically sensible and computationally efficient, and accommodate testing at multiple resolutions. Finally, applications are made to change point detection and medical image classification. |
