  Probabilistic Circuits (PCs) have emerged as an efficient framework for
representing and learning complex probability distributions. Nevertheless, the
existing body of research on PCs predominantly concentrates on data-driven
parameter learning, often neglecting the potential of knowledge-intensive
learning, a particular issue in data-scarce/knowledge-rich domains such as
healthcare. To bridge this gap, we propose a novel unified framework that can
systematically integrate diverse domain knowledge into the parameter learning
process of PCs. Experiments on several benchmarks as well as real world
datasets show that our proposed framework can both effectively and efficiently
leverage domain knowledge to achieve superior performance compared to purely
data-driven learning approaches.
