Results 231 to 240 of about 23,325 (286)

MARKOV CHAIN METHOD FOR COMPUTING THE RELIABILITY OF HAMMOCK NETWORKS

Probability in the Engineering and Informational Sciences, 2020
In this paper, we develop a new method for evaluating the reliability polynomial of a hammock network. The method is based on a homogeneous absorbing Markov chain and provides the exact reliability for networks of width less than 5 and arbitrary length.
Marilena Jianu, Daniel Ciuiu, Leonard Dăuş, Mihail Jianu   +3 more
openaire   +2 more sources

Evolutionary Markov Dynamics for Network Community Detection

IEEE Transactions on Knowledge and Data Engineering, 2022
Community structure division is a crucial problem in the field of network data analysis. Algorithms based on Markov chains are easy to use and provide promising solutions for community detection.
Zhen Wang, Chunyu Wang, Xianghua Li, Chao Gao, Xuelong Li, Junyou Zhu   +5 more
semanticscholar   +1 more source

Protein Subcellular Localization Prediction Based on Internal Micro-similarities of Markov Chains

Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2019
Elucidating protein subcellular localization is an essential topic in proteomics research due to its importance in the process of drug discovery. Unfortunately, experimentally uncovering protein subcellular targets is an arduous process that may not ...
Asem Alaa, A. Eldeib, Ahmed A. Metwally
semanticscholar   +1 more source

Comparison of Some Direct Methods for Computing Stationary Distributions of Markov Chains

SIAM Journal on Scientific and Statistical Computing, 1984
The purpose of this paper is to report on a comparison of an implementation of a simple direct LU factorization method, suggested by \textit{R. E. Funderlic} and \textit{J. B. Mankin} [ibid. 2, 375-383 (1981; Zbl 0468.65042)], with other direct methods recommended by \textit{C. C. Paige} and \textit{G. P. H. Styan} and \textit{P. G.
Harrod, W. J., Plemmons, R. J.
openaire   +1 more source

Markov Chain Monte Carlo Methods for Computing Bayes Factors

Journal of the American Statistical Association, 2001
The problem of calculating posterior probabilities for a collection of competing models and associated Bayes factors continues to be a formidable challenge for applied Bayesian statisticians. Current approaches that take advantage of modern Markov chain Monte Carlo computing methods include those that attempt to sample over some form of the joint space
Han C., Carlin B. P.
openaire   +2 more sources

Computer simulations of the structure of amorphous systems by the Markov chain method

Journal of Physics F: Metal Physics, 1984
An efficient method for computer simulations of liquid and amorphous systems is described. The method is based on replacing the natural trajectory of the system in phase space generated by the Newtonian equations of motion by a Markov chain. Convergence to the Gibbs distribution is proved. The relation to the other simulation methods is discussed.
M Krajci, P Mrafko
openaire   +1 more source

Computing multiobjective Markov chains handled by the extraproximal method

Annals of Operations Research, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Aggregation/Disaggregation Methods for Computing the Stationary Distribution of a Markov Chain

SIAM Journal on Numerical Analysis, 1987
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Merging Jacobi and Gauss-Seidel methods for solving Markov chains on computer clusters

2008 International Multiconference on Computer Science and Information Technology, 2008
The authors consider the use of the parallel iterative methods for solving large sparse linear equation systems resulting from Markov chains-on a computer cluster. A combination of Jacobi and Gauss-Seidel iterative methods is examined in a parallel version. Some results of experiments for sparse systems with over 3 times 107 equations and about 2 times
Jaroslaw Bylina, Beata Bylina
openaire   +1 more source

Iterative Aggregation/Disaggregation Methods for Computing Some Characteristics of Markov Chains

2001
A class of iterative aggregation/disaggregation methods (IAD) for computation some important characteristics of Markov chains such as stationary probability vectors and mean first passage times matrices is presented and convergence properties of the corresponding algorithms are analyzed.
Ivo Marek, Petr Mayer
openaire   +1 more source