  Nested sampling has been the workhorse for estimating parameters of compact
binaries from the gravitational wave signals. It operates using a set of live
points and accepts/rejects points drawn from prior such that the live point
with the lowest likelihood is replaced by a point with a higher likelihood
value. Each such iteration shrinks the volume enclosed by the live point of
lowest likelihood by a known factor. The estimated sampling density along with
the likelihood values of the new and old live points quantifies the probability
mass enclosed by them. Although robust, nested sampling often discards a
majority of the sampled points~($\sim 99.9\%$) at which likelihood was
calculated. However, for small dimensional problems, which happens to be the
case for compact binaries, the sampling density of all the sampled points can
be explicitly calculated thereby removing the need to discard samples. The
points' sampling density and likelihood values constitute the posterior
distribution. Avoiding the wastage implies significant improvement in the
sampling efficiency. In a previous method, we presented a recipe to reconstruct
the live volume; the volume that encloses most of the posterior probability
mass. We build on this method and present a methodology that explicitly records
the sampling density of all the points. For small dimensions, the presented
sampler is significantly more efficient than the canonical nested samplers and
is embarrassingly parallel.
