混合粒子滤波的推理计划
Inference Plans for Hybrid Particle Filtering
AI总结：
本文提出推理计划接口及Siren语言，使开发者能控制混合粒子滤波中随机变量的划分，通过静态分析验证计划可满足性，平均提速1.76倍、精度提升1.83倍。
摘要：
采用混合粒子滤波的高级概率编程语言（PPL）将符号精确推理与蒙特卡洛方法相结合，以提高推理性能。这些系统使用启发式方法将程序中的随机变量划分为以符号方式编码的变量和以采样值编码的变量，而启发式方法不一定与开发者的性能评估指标一致。在本工作中，我们提出了推理计划，这是一种编程接口，使开发者能够在混合粒子滤波过程中控制随机变量的划分。我们进一步提出了Siren，一种新的PPL，使开发者能够使用注解来指定推理系统必须实现的推理计划。为了帮助开发者静态地推理推理计划是否可实现，我们提出了一种基于抽象解释的Siren静态分析，用于确定推理计划的可满足性。我们证明了该分析相对于Siren的语义是可靠的。我们的评估将推理计划应用于三种不同的混合粒子滤波算法，并在基准测试套件上进行测试。结果表明，与默认启发式方法实现的推理计划相比，推理计划提供的控制能够实现平均1.76倍、最高206倍的加速以达到目标精度；结果还表明，与默认推理计划相比，推理计划在相同或更短的运行时间内平均提高了1.83倍、最高595倍的精度。我们进一步表明，我们的静态分析在实践中是精确的，在33个基准算法评估设置中，识别出了27个所有可满足的推理计划。
英文摘要：
Advanced probabilistic programming languages (PPLs) using hybrid particle filtering combine symbolic exact inference and Monte Carlo methods to improve inference performance. These systems use heuristics to partition random variables within the program into variables that are encoded symbolically and variables that are encoded with sampled values, and the heuristics are not necessarily aligned with the developer's performance evaluation metrics. In this work, we present inference plans, a programming interface that enables developers to control the partitioning of random variables during hybrid particle filtering. We further present Siren, a new PPL that enables developers to use annotations to specify inference plans the inference system must implement. To assist developers with statically reasoning about whether an inference plan can be implemented, we present an abstract-interpretation-based static analysis for Siren for determining inference plan satisfiability. We prove the analysis is sound with respect to Siren's semantics. Our evaluation applies inference plans to three different hybrid particle filtering algorithms on a suite of benchmarks. It shows that the control provided by inference plans enables speed ups of 1.76x on average and up to 206x to reach a target accuracy, compared to the inference plans implemented by default heuristics; the results also show that inference plans improve accuracy by 1.83x on average and up to 595x with less or equal runtime, compared to the default inference plans. We further show that our static analysis is precise in practice, identifying all satisfiable inference plans in 27 out of the 33 benchmark-algorithm evaluation settings.
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