Quickest linear search over correlated sequences

Linear search arises in many application domains. The problem of linear search over multiple sequences in order to identify one sequence with a desired statistical feature is considered. The quickest linear search optimizes a balance between two opposing performance measures, one being the delay in detecting a desirable sequence, and the other one being the quality of the decision. The existing approaches in the quickest search literature rely on the assumption that the sequences are statistically independent. In many applications, however, due to the underlying physical couplings, generations of available sequences are not necessarily independent. Driven by such underlying couplings, this paper considers searching over correlated sequences, in which the distribution of each sequence depends on the distribution of its preceding one. The closed-form characterization of the sampling process for the optimal search is delineated. The analysis reveals that depending on the correlation structure, the optimal search strategy can be similar to (in spirit) or dramatically different from the optimal search strategy over independent sequences.