  In the Wiener disorder problem, the drift of a Wiener process changes
suddenly at some unknown and unobservable disorder time. The objective is to
detect this change as quickly as possible after it happens. Earlier work on the
Bayesian formulation of this problem brings optimal (or asymptotically optimal)
detection rules assuming that the prior distribution of the change time is
given at time zero, and additional information is received by observing the
Wiener process only. Here, we consider a different information structure where
possible causes of this disorder are observed. More precisely, we assume that
we also observe an arrival/counting process representing external shocks. The
disorder happens because of these shocks, and the change time coincides with
one of the arrival times. Such a formulation arises, for example, from
detecting a change in financial data caused by major financial events, or
detecting damages in structures caused by earthquakes. In this paper, we
formulate the problem in a Bayesian framework assuming that those observable
shocks form a Poisson process. We present an optimal detection rule that
minimizes a linear Bayes risk, which includes the expected detection delay and
the probability of early false alarms. We also give the solution of the
``variational formulation'' where the objective is to minimize the detection
delay over all stopping rules for which the false alarm probability does not
exceed a given constant.
