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A computationally feasible methodology will be presented for state estimation and failure identification in discrete-time nonlinear stochastic systems. The procedure involves using multiple hypothesis testing to isolate failures in both system inputs and outputs. The method is obtained by extending Friedland's results [1] for separate bias estimation and Caglayan's results [2] for simultaneous detection...