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As per inferential approach both are estimation problem. But, in signal recovery, we estimate our input signal from the measured (noisy or noise free) observations. And, in parameter estimation, we estimate parameter for a particular model structure (based on prior knowledge and characterization of data) from observed dateset. Apart from this very basic similarity and difference, do we have any other concepts to explore these things in much more detail?

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