Sensing, Signals, and Communication Seminar
Wednesday, April 20, 2016
3:30 pm - 5:00 pm
Gross Hall 330
Information acquisition problems form a class of stochastic decision problems in which a decision maker is faced with utilizing a stochastically varying (and uncontrollable) environment. However, the state of the environment, due to the limited nature of the measurements in terms of dimension/ complexity/cost/accuracy, is only partially known to the decision maker. The decision maker, by carefully controlling the sequence of actions with uncertain outcomes and noisy measurements, dynamically refines the belief about the stochastically varying parameters of interest. This problem arises in a broad spectrum of applications such as medical diagnosis, cognitive radio, sensor management, active learning, generalized noisy search, and noisy group testing. A generalization of hidden Markov models and partially observable Markov models, information acquisition is both an informational problem as well as a control one. In this talk, we start with active hypothesis testing as a special case of information acquisition. This problem has been studied in various areas of applied mathematics, statistics, and engineering.