We show that“full-bang”control is optimal in a problem which combines features of(i)sequential least-squares estimation with Bayesian updating,for a random quantity observed in a bath of white noise;(ii)bounded cont...We show that“full-bang”control is optimal in a problem which combines features of(i)sequential least-squares estimation with Bayesian updating,for a random quantity observed in a bath of white noise;(ii)bounded control of the rate at which observations are received,with a superquadratic cost per unit time;and(iii)“fast”discretionary stopping.We develop also the optimal filtering and stopping rules in this context.展开更多
基金Erik Ekström acknowledges the support from the Swedish Research Council(Grant No.2019-03525)Ioannis Karatzas acknowledges the support from the National Science Foundation(Grant No.NSF-DMS-20-04977).
文摘We show that“full-bang”control is optimal in a problem which combines features of(i)sequential least-squares estimation with Bayesian updating,for a random quantity observed in a bath of white noise;(ii)bounded control of the rate at which observations are received,with a superquadratic cost per unit time;and(iii)“fast”discretionary stopping.We develop also the optimal filtering and stopping rules in this context.
