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We present a method for inferring the topology of a sensor network given nondiscriminating observations of activity in the monitored region. This is accomplished based on no prior knowledge of the relative locations of the sensors and weak assumptions regarding environmental conditions. Our approach employs a two-level reasoning system made up of a stochastic expectation maximization algorithm and a higher level search strategy employing the principle of Occam's Razor to look for the simplest solution explaining the data. The result of the algorithm is a Markov model describing the behavior of agents in the system and the underlying traffic patterns. Numerical simulations and experimental assessment conducted on a real sensor network suggest that the technique could have promising real-world applications in the area of sensor network self-configuration.