An SMT-Based Perfect Sampling Algorithm for Stochastic Petri Nets

H. Okamura, Kazuya Morihara, T. Dohi
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Abstract

This paper proposes a perfect sampling algorithm for stochastic Petri nets (SPN). The perfect sampling is a technique to draw samples exactly following the stationary distribution. The paper develops the enveloped perfect sampling algorithm (EPSA) for SPN. The main idea behind our approach is to formulate the mathematical programming to obtain lower and upper bounds of system states which are required by EPSA, and the problem is solved by using SMT (satisfiability modulo theories) solver. The presented SMT-based EPSA for SPN expands the applicability of perfect sampling algorithm for SPNs.
基于smt的随机Petri网完美抽样算法
针对随机Petri网(SPN)提出了一种完善的采样算法。完美抽样是一种精确地按照平稳分布绘制样本的技术。提出了一种适用于SPN的包络完美采样算法。该方法的主要思想是通过制定数学规划来获得EPSA所要求的系统状态的下界和上界,并利用SMT(可满足模理论)求解器进行求解。本文提出的基于smt的SPN EPSA扩展了SPN完美采样算法的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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