TY - CHAP

T1 - Random Samplings Using Metropolis Hastings Algorithm

AU - Arcos-Argudo, Miguel

AU - Bojorque-Chasi, Rodolfo

AU - Plaza-Cordero, Andrea

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Markov chains

KW - Metropolis hastings

KW - Node sampling

KW - Random sampling

KW - Random walks

KW - Small worlds

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85067696504&origin=inward

UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85067696504&origin=inward

UR - http://www.mendeley.com/research/random-samplings-using-metropolis-hastings-algorithm

U2 - 10.1007/978-3-030-20454-9_11

DO - 10.1007/978-3-030-20454-9_11

M3 - Chapter

SN - 9783030204532

T3 - Advances in Intelligent Systems and Computing

SP - 114

EP - 122

BT - Advances 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

A2 - Ahram, Tareq

Y2 - 1 January 2015

ER -