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

控制不确定性：信念的顺序细化本研究侧重于决策者通过仔细控制一系列具有不确定结果的行动，以快速、准确和有效的方式动态地细化他对感兴趣的现象的信念的各种信息获取和传感应用。该模型包括通信、实验设计、认知科学和传感器管理方面的一类应用。重点解决了以下三个问题。主动序贯假设检验：有一组假设，其中一个是正确的。要求决策者通过顺序地采用可用的感知动作之一来识别正确的假设。行动的成本不同，并产生统计上不同的观察结果。考虑到对声明错误的惩罚，该工作研究了感知行为的最佳选择。带带宽扩展的联合信源信道编码的反馈方案：通过带反馈的无噪声信道将消息传送到接收机。信息和接收者之间预期的扭曲？S结构在因果编码函数和解码函数的选择上力求最小化。带反馈的多址信道上的联合源信道编码：多台发射机通过具有完美输出反馈的噪声无记忆多址信道将消息传递给公共接收器。这些问题归结为动态系统的顺序控制，该系统是未知（真实假设，信息等）的条件分布，其动力学由贝叶斯？规则。特别地，研究了行动的最优选择，即条件分布的细化和不确定性的减少。

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.