In this paper, we derive Hybrid, Bayesian and Marginalized Cram\'{e}r-Rao
lower bounds (HCRB, BCRB and MCRB) for the single and multiple measurement
vector Sparse Bayesian Learning (SBL) problem of estimating compressible
vectors and their prior distribution parameters. We assume the unknown vector
to be drawn from a compressible Student-t prior distribution. We derive CRBs
that encompass the deterministic or random nature of the unknown parameters of
the prior distribution and the regression noise variance. We extend the MCRB to
the case where the compressible vector is distributed according to a general
compressible prior distribution, of which the generalized Pareto distribution
is a special case. We use the derived bounds to uncover the relationship
between the compressibility and Mean Square Error (MSE) in the estimates.
Further, we illustrate the tightness and utility of the bounds through
simulations, by comparing them with the MSE performance of two popular
SBL-based estimators. It is found that the MCRB is generally the tightest among
the bounds derived and that the MSE performance of the Expectation-Maximization
(EM) algorithm coincides with the MCRB for the compressible vector. Through
simulations, we demonstrate the dependence of the MSE performance of SBL based
estimators on the compressibility of the vector for several values of the
number of observations and at different signal powers.Comment: Accepted for publication in the IEEE Transactions on Signal
Processing, 11 pages, 10 figure
