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.