We show that "full-bang" control is optimal in a problem that combines features of (i) sequential least-squares {\it estimation} with Bayesian updating, for a random quantity observed in a bath of white noise; (ii) bounded {\it control} of the rate at which observations are received, with a superquadratic cost per unit time; and (iii) "fast" discretionary {\it stopping}. We develop also the optimal filtering and stopping rules in this context.

翻译：我们显示,“全干”控制是最佳的,因为这一问题结合了以下特点:(一) 连续最低面积估计值与巴伊西亚的更新,在白色噪音浴缸中随机观测到的数量;(二) 以每单位时间超赤道成本作为接收观测的速率的界限;以及(三) “快速”自由裁量 {它停止}。 我们还在这方面制定了最佳过滤和阻止规则。