  The problem of detecting a single anomalous process among multiple
independent processes is considered. Under a constraint on the number of
processes that can be probed simultaneously, the decision maker should decide
which processes to probe at each time and when to terminate the probing.
Compared with previous work considering only the observation costs, the
switching costs of switchings across processes also need to be taken into
account in many practical scenarios. The objective is an active inference
strategy that minimizes the Bayesian risk taking into account of the sample
complexity, switching cost, as well as detection errors. Based on the framework
of sequential design of experiments, we propose a low-complexity, low-switching
deterministic policy for two scenarios where the total switching cost is
negligible and the total switching cost is comparable to the total observation
cost. We show that the proposed algorithm is asymptotically optimal in the
former scenario and is order optimal in the latter scenario. Simulation results
demonstrate strong performance in the finite regime for both scenarios.
