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It is well known that the performance of sparse vector recovery algorithmsfrom compressive measurements can depend on the distribution underlying thenon-zero elements of a sparse vector. However, the extent of these effects hasyet to be explored, and formally presented. In this paper, I empiricallyinvestigate this dependence for seven distributions and fifteen recoveryalgorithms. The two morals of this work are: 1) any judgement of the recoveryperformance of one algorithm over that of another must be prefaced by theconditions for which this is observed to be true, including sparse vectordistributions, and the criterion for exact recovery; and 2) a recoveryalgorithm must be selected carefully based on what distribution one expects tounderlie the sensed sparse signal.

Sparse Vector Distributions and Recovery from Compressed Sensing