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Sunday, July 26, 2020 | History

3 edition of A multi-level solution algorithm for steady-state Markov chains found in the catalog.

A multi-level solution algorithm for steady-state Markov chains

A multi-level solution algorithm for steady-state Markov chains

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  • 33 Currently reading

Published by National Aeronautics and Space Administration, Langley Research Center, National Technical Information Service, distributor in Hampton, Va, [Springfield, Va .
Written in English

    Subjects:
  • Markov processes -- Numerical solutions.

  • Edition Notes

    StatementGraham Horton, Scott T. Leutenegger.
    SeriesICASE report -- no. 93-81., NASA contractor report -- 191558., NASA contractor report -- NASA CR-191558.
    ContributionsLeutenegger, Scott T., Langley Research Center.
    The Physical Object
    FormatMicroform
    Pagination1 v.
    ID Numbers
    Open LibraryOL15389053M

    BOOK OF ABSTRACTS August, Baku, Azerbaijan of the pumping stations, the range of admissible values of the control actions, and the values of the initial and final steady-state regimes ([1]). C 1 (0, l, R n), z () C 1 (l1, 2l, R n).The algorithm of solution of this problem is given and this algorithm is applied to gas. CHAPTER 2 LITERATURE REVIEW 18 Decision trees 18 Markov cycle trees 19 Stochastic trees 20 Markov models 21 Dynamic decision models 22 Obtaining the numbers 28 Static modeling 29 CHAPTER 3 RESEARCH OBJECTIVES 32 Problem statement 32 Research objectives 33 CHAPTER 4 PROBLEM FORMULATION AND SOLUTION METHODOLOGY.

    Perfect sampling for nonhomogeneous Markov chains and hidden Markov models. Nicolas Broutin, Luc Devroye, Gábor Lugosi. Almost optimal sparsification of random geometric graphs. Ari Arapostathis, Guodong Pang. Ergodic diffusion control of multiclass multi-pool networks in the Halfin–Whitt regime. In thispaper we study a Monte Carlo simulation--based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and the expected value function is approximated by the corresponding sample average by:

    %%% -*-BibTeX-*- %%% ===== %%% BibTeX-file{ %%% author = "Nelson H. F. Beebe", %%% version = "", %%% date = "28 January ", %%% time = " MST. An introduction to essential elements of instrumentation and sensing technology. Topics include embedded system programming, basic inputs such as sensors, switches, and keyboards; basic outputs such as motors, relays, LEDs, displays, and speakers; associated circuitry for inputs and outputs; the basics of communications between devices; and power supplies such as linear, switching, and batteries.


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A multi-level solution algorithm for steady-state Markov chains Download PDF EPUB FB2

A new iterative algorithm, the multi-level algorithm, for the numerical solution of steady state Markov chains is presented. The method utilizes a set of recursively coarsened representations of. This paper illustrates the current state of development of an algorithm for the steady state solution of continuous-time Markov chains.

The so-called multi-level algorithm utilizes ideas from. Get this from a library. A multi-level solution algorithm for steady-state Markov chains. [Graham Horton; Scott T Leutenegger; Langley Research Center.]. In probability theory, a nearly completely decomposable (NCD) Markov chain is a Markov chain where the state-space can be partitioned in such a way that movement within a partition occurs much more frequently than movement between partitions.

Particularly efficient algorithms exist to compute the stationary distribution of Markov chains with this property. Horton, S. Leutenegger: A Multilevel Solution Algorithm for Steady-State Markov Chains, Proceedings of the ACM SIGMETRICS Conference on Measurement and Modeling of Computer Systems, Nashville, TN, May 16–20, Google ScholarCited by: Get this from a library.

On the utility of the multi-level algorithm for the solution of nearly completely decomposable Markov chains. [Scott T Leutenegger; Graham Horton; Institute for Computer Applications in Science and Engineering.]. Computations with Markov Chains presents the edited and reviewed proceedings of the Second International Workshop on the Numerical Solution of Markov Chains, held January, in Raleigh, North Carolina.

New developments of particular interest include recent work on stability and conditioning, Krylov subspace-based methods for transient solutions, quadratic convergent Author: William J. Stewart. Computations with Markov Chains presents the edited and reviewed proceedings of the Second International Workshop on the Numerical Solution of Markov Chains, held January 16&#;18,in Raleigh, North Carolina.

New developments of particular interest include recent work on stability and Price: $ A multi-level solution algorithm for steady-state Markov chains.

In: Gaither, B.D. (ed.), Proceedings of the ACM SIGMETRICS Conference on Measurement and Modeling of Computer Systems, pp. – () Google ScholarAuthor: Francisco Macedo. Numerical Solution of Large Finite Markov Chains by Algebraic Multigrid Techniques; U.R.

Krieger. On the Utility of the Multi-Level Algorithm for the Solution of Nearly Completely Decomposable Markov Chains; S.T. Leutenegger, G. Horton. Author: William J. Stewart. Computations with Markov Chains presents the edited and reviewed proceedings of the Second International Workshop on the Numerical Solution of Markov Chains, held January, in Raleigh, North Carolina.

New developments of particular interest include recent work on stability and conditioning, Krylov subspace-based methods for transient solutions, quadratic convergent.

An overview of the first theoretical results for the IAD methods for solutions of characteristics of Markov chains can be found in the book From the description of the multi-level IAD algorithm it readily follows that the exact solution x S.T.

LeuteneggerA multi-level solution algorithm for Cited by: 8. @article{osti_, title = {An adaptive multi-level simulation algorithm for stochastic biological systems}, author = {Lester, C., E-mail: [email protected] and Giles, M.

and Baker, R. and Yates, C. A.}, abstractNote = {Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Chapter 3 presents the steady-state solution of ergodic Markov chains, focusing on symbolic solutions, non-Markovian queues, numerical solutions—the most important direct and interactive methods—and a comparison of numerical methods.

In chapter 4, the authors deal with steady-state aggregation and disaggregation methods. Experimental results for large, sparse Markov chains, especially the ill-conditioned nearly completely decomposable (NCD) ones, are few.

We believe there is need for further research in this area, specifically to aid in the understanding of the effects of the degree of coupling of NCD Markov chains and their nonzero structure on the convergence characteristics and space requirements of Cited by: On the Utility of the Multi-Level Algorithm for the Solution of Nearly Completely Decomposable Markov Chains Preconditioned Krylov Subspace Methods for the Numerical Solution of Markov Chains A Parallel Block Projection Method of the Cimmino Type for Finite Markov ChainsCited by: A new iterative algorithm, the multi-level algorithm, for the numerical solution of steady state Markov chains is presented.

The method utilizes a set of recursively coarsened representations of the original system to achieve accelerated Cited by: 5. Peter Müller, in International Encyclopedia of the Social & Behavioral Sciences (Second Edition), Abstract.

Markov chain Monte Carlo (MCMC) methods use computer simulation of Markov chains in the parameter space.

The Markov chains are defined in such a way that the posterior distribution in the given statistical inference problem is the asymptotic distribution.

edition, John Wiley, (Blue book) –Chinese translation, ; fully revised paperback, Performance and Reliability Analysis of Computer Systems: An Example-Based Approach Using the SHARPE Software Package, Kluwer, (Red book) Queuing Networks and Markov Chains, John Wiley, second edition, (White book)File Size: 2MB.

Chapter 9 recalls the basic results on continuous-time Markov chains and how they apply to the evaluation of several availability measures.

It contains interesting examples and case studies as well. Both the sensitivity analysis with respect to a given parameter and numerical methods for steady-state analysis are presented. Spectral Theory for Skip-Free Markov Chains.

Probability in the Engineering and Information Sciences, vol. 3, No. 1,Network Design and Control Using On-Off and Multi-Level Source Traffic Models with Heavy-Tailed Distributions. The Steady-State Distribution of the M t /M/infty Queue with a Sinusoidal Arrival Rate.This self-contained guide provides comprehensive coverage of all the analytical and modeling techniques currently in use, from classical non-state and state space approaches, to newer and more advanced methods such as binary decision diagrams, dynamic fault trees, Bayesian belief networks, stochastic Petri nets, non-homogeneous Markov chains.Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the state-of-the-art theory, methods and applications that have emerged in Monte Carlo simulation since the publication of the classic First Edition over more than a quarter of a century ago.