期刊
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
卷 -, 期 -, 页码 -出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2022.2025841
关键词
Bayesian inference; Gibbs sampling; Hamiltonian Monte Carlo; M; M; 1 queue model; the No-U-Turn sampler; traffic intensity
资金
- National Natural Science Foundation of China [11971486, 11771452]
- Natural Science Foundation of Hunan [2019JJ40357, 2020JJ4674]
This paper uses the NUTS algorithm to perform Bayesian inference for the traffic intensity of the M/M/1 queue. The results show that the NUTS algorithm outperforms other algorithms.
Traffic intensity is one of the most critical parameters of single-server Markovian queues. This paper deals with the Bayesian inference for the M/M/1 queue by sampling from the posterior distribution. The No-U-Turn Sampler (NUTS) is a recently developed Markov Chain Monte Carlo (MCMC) algorithm, which is proposed to compute the traffic intensity by observing the number of customers in the system at the departure epoch. Numerical results show that the NUTS outperforms the other algorithms in the literature.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据