- Keywords: probabilistic reasoning, tractable inference, probabilistic circuits
- TL;DR: By characterizing the tractability of simple operations over circuits we can build a modular atlas of tractable model classes for complex queries.
- Abstract: Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models. In this paper, we show how complex inference scenarios for these models that commonly arise in machine learning---from computing the expectations of decision tree ensembles to information-theoretic divergences of sum-product networks---can be represented in terms of tractable modular operations over circuits. Specifically, we characterize the tractability of simple transformations---sums, products, powers, logarithms, and exponentials---in terms of sufficient structural constraints of the circuits they operate on. Building on these operations, we derive a unified framework for reasoning about tractable models that generalizes several results in the literature and opens up novel tractable inference scenarios.