提出通用因子接口，让模型自由混合离散连续表示。
Towards representation agnostic probabilistic programming
- 用五个基本操作定义因子抽象，解耦表示与推断算法
- 支持在统一框架内混合离散表、高斯分布等不同表示
- 适合研究复杂混合模型的学者和需要灵活建模的开发者
当前概率编程语言将模型表示与特定推断算法紧密耦合，限制了新型表示或混合离散-连续模型的探索。本文提出一种因子抽象，包含五种基本操作，作为与底层表示无关的通用接口，实现表示无关的概率编程。用户可在单一统一框架中自由混合离散表格、高斯分布、基于采样的方法等不同表示，从而在现有工具难以表达的复杂混合模型上实现实用推断。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。