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
T2 - Advances in Intelligent Systems and Computing
Y2 - 1 January 2015
ER -