  We revisit the classical problem of exact inference on probabilistic
graphical models (PGMs). Our algorithm is based on recent worst-case optimal
database join algorithms, which can be asymptotically faster than traditional
data processing methods. We present the first empirical evaluation of these new
algorithms via JoinInfer, a new exact inference engine. We empirically explore
the properties of the data for which our engine can be expected to outperform
traditional inference engines refining current theoretical notions. Further,
JoinInfer outperforms existing state-of-the-art inference engines (ACE, IJGP
and libDAI) on some standard benchmark datasets by up to a factor of 630x.
Finally, we propose a promising data-driven heuristic that extends JoinInfer to
automatically tailor its parameters and/or switch to the traditional inference
algorithms.
