Sufficiency Theorem and Closed-Form Posterior for a Windowed Change Point Detection Task

Abstract

To function effectively in a changing world, agents must detect changes and act on them in a timely fashion. We study this in the context of a change point detection task in which clicks arrive from a Poisson process whose rate changes at an unknown time. An agent must declare the change within a detection window of fixed width or the trial ends in a miss. Here we prove a window sufficiency theorem, which states that the posterior probability of a change depends only on the clicks within the most recent window, regardless of the prior on the change time. This result follows from combining survival conditioning, which rules out change times whose windows have already expired, with cancellations driven by independent increments of a known baseline process. Window sufficiency generalizes to an unknown post-change process, as long as the baseline process is known. For an exponential prior on the change time, we further derive the posterior in closed form as a function of the elapsed times since each click in the most recent window, the known rates, and the window width. These results suggest that discarding old evidence can be optimal rather than a limitation when decisions face a deadline.