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Exact Sparse Super-Resolution Via Model Aggregation

This paper studies the problem of discrete super-resolution. Existing stability guarantees rely on the fact that certain separation conditions are satisfied by the true support. However, such structural conditions have not been exploited in the corresponding algorithmic designs. This paper proposes a novel Bayesian approach based on the model aggregation idea that can generate an exact sparse estimate, and maintain the required structures of the support. The proposed method is implemented within the MCMC framework and empirically provides better support recovery than available algorithms.