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Title: Fast Fourier Transform Reductions for Bayesian Network Inference
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 independence (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.  more » « less
Award ID(s):
2008516
PAR ID:
10376093
Author(s) / Creator(s):
; ;
Editor(s):
Camps-Valls, Gustau; Ruiz, Francisco J.; Valera, Isabel
Date Published:
Journal Name:
AI & Statistics, PMLR
Volume:
151
Page Range / eLocation ID:
6445-6458
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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