Random Samplings Using Metropolis Hastings Algorithm

Miguel Arcos-Argudo, Rodolfo Bojorque-Chasi, Andrea Plaza-Cordero

Research output: Chapter in Book/Report/Conference proceedingChapter


Random Walks Samplings are important method to analyze any kind of network; it allows knowing the network’s state any time, independently of the node from which the random walk starts. In this work, we have implemented a random walk of this type on a Markov Chain Network through Metropolis-Hastings Random Walks algorithm. This algorithm is an efficient method of sampling because it ensures that all nodes can be sampled with a uniform probability. We have determinate the required number of rounds of a random walk to ensuring the steady state of the network system. We concluded that, to determinate the correct number of rounds with which the system will find the steady state it is necessary start the random walk from different nodes, selected analytically, especially looking for nodes that may have random walks critics.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence, Software and Systems Engineering - Proceedings of the AHFE International Conference on Human Factors in Artificial Intelligence and Social Computing, the AHFE International Conference on Human Factors, Software, Service and Systems Engineering, and the AHFE International Conference of Human Factors in Energy, 2019
EditorsTareq Ahram
Number of pages9
ISBN (Electronic)9783030204532
StatePublished - 1 Jan 2020
EventAdvances in Intelligent Systems and Computing - , Germany
Duration: 1 Jan 2015 → …

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceAdvances in Intelligent Systems and Computing
Period1/01/15 → …


  • Markov chains
  • Metropolis hastings
  • Node sampling
  • Random sampling
  • Random walks
  • Small worlds


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