Current probabilistic programming languages and tools tightly couple model representations with specific inference algorithms, preventing experimentation with novel representations or mixed discrete-continuous models. We introduce a factor abstraction with five fundamental operations that serve as a universal interface for manipulating factors regardless of their underlying representation. This enables representation-agnostic probabilistic programming where users can freely mix different representations (e.g. discrete tables, Gaussians distributions, sample-based approaches) within a single unified framework, allowing practical inference in complex hybrid models that current toolkits cannot adequately express.

翻译：当前的概率编程语言和工具将模型表示与特定推理算法紧密耦合，阻碍了对新型表示或混合离散-连续模型的实验探索。我们引入了一种因子抽象，其包含五种基本操作，可作为操作因子的通用接口，而不受其底层表示形式的限制。这实现了表示无关的概率编程，用户可以在单一统一框架内自由混合不同表示形式（例如离散表格、高斯分布、基于采样的方法），从而在当前工具包无法充分表达的复杂混合模型中实现实用推理。