We present \textsc{ISymb} an incremental
symbolic inference framework
for probabilistic programs in situations when
some loop-manipulated array data, upon
which their probabilistic models are conditioned,
undergoes small changes. To tackle the path
explosion challenge, \textsc{ISymb} is intra-procedurally
path-sensitive except
that it conducts a ``meet-over-all-paths''
analysis within an iteration of a loop
(conditioned on some observed array data).
By recomputing only the
probability distributions for the
paths affected, \textsc{ISymb} avoids
expensive symbolic inference from scratch while
also being
precision-preserving. Our evaluation with a set of
existing benchmarks shows that
\textsc{ISymb} can lead to orders of magnitude
performance improvements compared to its
non-incremental counterpart (under small changes in
observed array data).