  The paper introduces a novel methodology for the identification of
coefficients of switched autoregressive linear models. We consider the case
when the system's outputs are contaminated by possibly large values of
measurement noise. It is assumed that only partial information on the
probability distribution of the noise is available. Given input-output data, we
aim at identifying switched system coefficients and parameters of the
distribution of the noise which are compatible with the collected data. System
dynamics are estimated through expected values computation and by exploiting
the strong law of large numbers. We demonstrate the efficiency of the proposed
approach with several academic examples. The method is shown to be extremely
effective in the situations where a large number of measurements is available;
cases in which previous approaches based on polynomial or mixed-integer
optimization cannot be applied due to very large computational burden.
