Weighted model integration (WMI) extends weighted model counting (WMC) in providing a computational abstraction for probabilistic inference in mixed discrete-continuous domains. WMC has emerged as an assembly language for state-of-the-art reasoning in Bayesian networks, factor graphs, probabilistic programs and probabilistic databases. In this regard, WMI shows immense promise to be much more widely applicable, especially as many real-world applications involve attribute and feature spaces that are continuous and mixed. Nonetheless, state-of-the-art tools for WMI are limited and less mature than their propositional counterparts. In this work, we propose a new implementation regime that leverages propositional knowledge compilation for scaling up inference. In particular, we use sentential decision diagrams, a tractable representation of Boolean functions, as the underlying model counting and model enumeration scheme. Our regime performs competitively to state-of-the-art WMI systems, but is also shown, for the first time, to handle non-linear constraints over non-linear potentials.

本文提出了新的实现方案，利用命题知识编译来扩展推理，使用可处理布尔函数的可决策图作为基础模型计数和列举方案，在非线性势函数上处理非线性约束，并与最先进的WMI系统竞争。

通过利用知识编译在线性和非线性混合领域中扩大概率推断