Hypertree Decompositions Revisited for PGMs
This addresses the problem of computational efficiency in exact inference for probabilistic graphical models, representing an incremental improvement with strong empirical gains.
The paper tackles exact inference on probabilistic graphical models by developing JoinInfer, an inference engine based on worst-case optimal database join algorithms, which outperforms existing state-of-the-art engines by up to 630x on some benchmark datasets.
We revisit the classical problem of exact inference on probabilistic graphical models (PGMs). Our algorithm is based on recent \emph{worst-case optimal database join} algorithms, which can be asymptotically faster than traditional data processing methods. We present the first empirical evaluation of these 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.