Fast Fourier Transform Reductions for Bayesian Network Inference
Fast Fourier Transform Reductions for Bayesian Network Inference
复制标题
DOI：
--
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Vincent Hsiao;Dana S. Nau;R. Dechter
中科院分区：
文献类型：
--
作者：
Vincent Hsiao;Dana S. Nau;R. Dechter
贝叶斯网络对于分析具有大量交互代理的系统的属性是有用的(例如，在社会建模应用和分布式服务应用中)。这些网络通常具有大函数(CPT)，使得精确的推理变得困难。然而，这些模型通常具有加性对称性。在这篇文章中，我们展示了如何通过使用快速傅立叶变换(ffi)来准确地计算基于求和的CPT，特别是在存在对称性的情况下。特别地，我们提出了一种使用快速傅立叶变换来减少基于和的因果相关(CI)的贝叶斯网络中条件概率表(ffi)的大小的有效方法。我们展示了如何将它直接应用于Bucket消元的加速，并随后提供了实验结果，证明了我们的方法提供的计算加速比。
Bayesian Networks are useful for analyzing the properties of systems with large populations of interacting agents (e.g., in social modeling applications and distributed service applications). These networks typically have large functions (CPTs), making exact inference intractable. However, often these models have additive symmetry. In this paper we show how summation-based CPTs, especially in the presence of symmetry, can be computed efficiently through the usage of the Fast Fourier Transform (FFT). In particular, we propose an efficient method using the FFT for reducing the size of Conditional Probability Tables (CPTs) in Bayesian Networks with summation-based causal in-dependence (CI). We show how to apply it directly towards the acceleration of Bucket Elimination, and we subsequently provide experimental results demonstrating the computational speedup provided by our method.