  Reverberation mapping (RM) is an important technique in studies of active
galactic nuclei (AGN). The key idea of RM is to measure the time lag $\tau$
between variations in the continuum emission from the accretion disc and
subsequent response of the broad line region (BLR). The measurement of $\tau$
is typically used to estimate the physical size of the BLR and is combined with
other measurements to estimate the black hole mass $M_{\rm BH}$. A major
difficulty with RM campaigns is the large amount of data needed to measure
$\tau$. Recently, Fine et al (2012) introduced a new approach to RM where the
BLR light curve is sparsely sampled, but this is counteracted by observing a
large sample of AGN, rather than a single system. The results are combined to
infer properties of the sample of AGN. In this letter we implement this method
using a hierarchical Bayesian model and contrast this with the results from the
previous stacked cross-correlation technique. We find that our inferences are
more precise and allow for more straightforward interpretation than the stacked
cross-correlation results.
