  In compressive sensing, sparse signals are recovered from underdetermined
noisy linear observations. One of the interesting problems which attracted a
lot of attention in recent times is the support recovery or sparsity pattern
recovery problem. The aim is to identify the non-zero elements in the original
sparse signal. In this article we consider the sparsity pattern recovery
problem under a probabilistic signal model where the sparse support follows a
Bernoulli distribution and the signal restricted to this support follows a
Gaussian distribution. We show that the energy in the original signal
restricted to the missed support of the MAP estimate is bounded above and this
bound is of the order of energy in the projection of the noise signal to the
subspace spanned by the active coefficients. We also derive sufficient
conditions for no misdetection and no false alarm in support recovery.
