CIF: Small: Controlling Uncertainty: On the Sequential Refinement of Belief

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Controlling Uncertainty: On the Sequential Refinement of BeliefThis research focuses on a variety of information acquisition and sensing applications in which adecision maker, by carefully controlling a sequence of actions with uncertain outcomes,dynamically refines his belief about a phenomenon of interest in a speedy, accurate, and efficientmanner. The model includes a class of applications in communications, design of experiments,cognitive science, and sensor management. In particular, the following three problems aretackled.? Active Sequential Hypothesis Testing: There are a set of hypotheses, one of which is true. Adecision maker is asked to identify the correct hypothesis by sequentially employing either oneof available sensing actions. Actions costs differently and produce statistically distinctobservations. Given a penalty for error in declaration, the work investigates the optimalselection of sensing actions.? Feedback Schemes for Joint Source-Channel Coding with Bandwidth Expansion: A message isto be conveyed to a receiver over a noisy memoryless channel with feedback. The expecteddistortion between the message and the receiver?s construction is sought to be minimized overthe choice of causal encoding functions as well as the decoding function.? Joint Source-Channel Coding over a Multiple Access Channel with Feedback: Multipletransmitters convey messages to a common receiver over a noisy memoryless multiple accesschannel with perfect output feedback.These problems boil down to the sequential control of a dynamical system whose system is theconditional distribution of the unknown (true hypothesis, message, etc) and whose dynamics isdictated by the Bayes? rule. In particular, the optimal choice of actions, i.e. refinement in theconditional distribution and reduction of uncertainty, is investigated.