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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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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.
    SeriesNASA contractor report -- 191558., ICASE report -- no. 93-81., NASA contractor report -- NASA CR-191558., ICASE report -- no. 93-81.
    ContributionsLeutenegger, Scott T., Langley Research Center.
    The Physical Object
    FormatMicroform
    Pagination1 v.
    ID Numbers
    Open LibraryOL14703268M

      An opinion-unaware blind quality assessment algorithm for multiply distorted images Tongle Wang ; Junchen Deng Proc. SPIE , Eleventh International Conference on Signal Processing Systems, (31 December ); doi: / Doctoral Dissertation Abstracts (A – L) Following is a complete list of doctoral graduates of the Department of Computer Science, with their dissertation titles. Graduates of other departments or schools, whose primary adviser was a member of the Department of Computer Science, are also listed.


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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 A multi-level solution algorithm for steady-state Markov chains book for the steady state solution of continuous-time Markov chains.

The so-called multi-level A multi-level solution algorithm for steady-state Markov chains book 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 A multi-level solution algorithm for steady-state Markov chains book 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.

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

First, we propose a model which contains two modified semi-Markov chains and a macro-Markov chain combined with the theory of M / G /1/ K queues to evaluate the characteristics of these two improved CSMA/CA schemes, in which traffic arrivals and accessing packets are bestowed with non-preemptive priority over each other, instead of Cited by: 4.

The generalization involves the acceptance of cost-increasing transitions with a nonzero probability to avoid getting stuck in local minima.

We prove that our algorithm asymptotically converges in probability to a globally minimal solution, despite the fact that the Markov chains generated by the algorithm are generally not by: 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.

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.

The local size of computational grids used in partial differential equation (PDE)-based probabilistic inverse problems can have a tremendous impact on the numerical results. As a consequence, numerical model identification procedures used in structural or material engineering may yield erroneous, mesh-dependent result.

In this work, we attempt to connect the field of adaptive methods for Cited by: 1. Bertsekas, "Centralized and Distributed Newton Methods for Network Optimization and Extensions," Lab. for Information and Decision Systems Report LIDS-P, MIT, April Abstract: We consider Newton methods for common types of single commodity and multi-commodity network flow problems.

Despite the potentially very large dimension of the problem, they can be implemented using the. Haider, S., D. Cho, R. Amelard, A. Wong, and D. Clausi, "Enhanced classification of malignant melanoma lesions via the integration of physiological features from dermatological photographs", 36th Annual International IEEE Engineering in Medicine and Biology Society Conference, Sheraton Chicago Hotel and Towers Chicago, IL, USA, 09/ BibTeX.

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Table of contents for issues of Journal of Numerical Linear Algebra with Applications Last update: Fri Oct 11 MDT Volume 1, Number 1, Volume 1, Number 2, Volume 1, Number 1, Volume 1, Number 2, Volume 1, Number 3, Volume 1. EE Markov Chains and Queuing Systems 3 0 0 6 Prerequisite: Background in Probability and Stochastic Processes and an interest in System Modeling.

Markov Chains and regenerative processes have been extensively used in modeling a wide variety of systems and phenomena. The first part of this book contains basic and classical material from the study of linear algebra and numerical linear algebra.

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