Convergence of Invariant Measures of Truncation Approximations to Markov Processes

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Applied Mathematics

ISSN Print: 2152-7385
ISSN Online: 2152-7393
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"Convergence of Invariant Measures of Truncation Approximations to Markov Processes"
written by Andrew G. Hart, Richard L. Tweedie,
published by Applied Mathematics, Vol.3 No.12A, 2012

has been cited by the following article(s):

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[1]	Numerical Methods for Integral Equations of the Second Kind with NonSmooth Solutions of Bounded Variation
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[2]	Padé SSA: A frequency domain method for estimating the dynamics of stochastic reaction networks
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[3]	Studies on the Conditional Stationary Distribution in Markov Chains via Systems of Linear Inequalities
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[4]	A Near-Optimal Finite Approximation Approach for Computing Stationary Distribution and Performance Measures of Continuous-State Markov Chains
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[5]	Postponing production exponentially enhances the molecular memory of a stochastic switch
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[6]	Characterization of the conditional stationary distribution in Markov chains via systems of linear inequalities
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[7]	Slack reactants: A state-space truncation framework to estimate quantitative behavior of the chemical master equation
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[8]	A finite state projection method for steady-state sensitivity analysis of stochastic reaction networks
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[9]	Bounding the stationary distributions of the chemical master equation via mathematical programming
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[10]	Stationary distributions of continuous-time Markov chains: a review of theory and truncation-based approximations
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[11]	A finite state projection algorithm for the stationary solution of the chemical master equation
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[12]	Error bounds for last-column-block-augmented truncations of block-structured Markov chains
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[13]	Deterministic approximation schemes with computable errors for the distributions of Markov chains
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[14]	Rigorous bounds on the stationary distributions of the chemical master equation via mathematical programming
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[15]	Error bounds for last-column-block-augmented truncations of blockstructured Markov chains
	Preprint, 2016

[16]	Error Bounds for Last-Block-Column-Augmented Truncations of Block-Structured Markov Chains
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[17]	The limit formula for the normalized fundamental matrix of the northwest-corner truncation of Markov chains: Matrix-infinite-product form solutions of block-Hessenberg Markov chains
	2016

[18]	The limit formula for the normalized fundamental matrix of the northwest-corner truncation of Markov chains: Matrix-infinite-product form solutions of block-Hessenberg …
	2016

[19]	Perturbation analysis for continuous-time Markov chains
	Science China Mathematics, 2015

[20]	连续时间 Markov 链的嵌入及其不变分布的数值计算
	中国科学: 数学, 2015

[21]	Continuous-time block-monotone Markov chains and their block-augmented truncations
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[22]	Error bounds for augmented truncations of discrete-time block-monotone Markov chains under subgeometric drift conditions
	arXiv preprint arXiv:1506.01789, 2015

[23]	Censoring technique and numerical computations of invariant distribution for continuous-time Markov chains
	Scientia Sinica …, 2015

[24]	Approximation stochastiques dans les systèmes de files d'attente
	2014

[1]	Postponing production exponentially enhances the molecular memory of a stochastic switch European Journal of Applied Mathematics, 2022 DOI:10.1017/S0956792520000479

[2]	Numerical Methods for Integral Equations of the Second Kind with NonSmooth Solutions of Bounded Variation SIAM Journal on Numerical Analysis, 2022 DOI:10.1137/22M1480422

[3]	Padé SSA: A frequency domain method for estimating the dynamics of stochastic reaction networks 2022 IEEE 61st Conference on Decision and Control (CDC), 2022 DOI:10.1109/CDC51059.2022.9992390

[4]	Refining Mean-field Approximations by Dynamic State Truncation Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2021 DOI:10.1145/3460092

[5]	Stationary Distributions of Continuous-Time Markov Chains: A Review of Theory and Truncation-Based Approximations SIAM Review, 2021 DOI:10.1137/19M1289625

[6]	Refining Mean-field Approximations by Dynamic State Truncation ACM SIGMETRICS Performance Evaluation Review, 2021 DOI:10.1145/3543516.3460099

[7]	Refining Mean-field Approximations by Dynamic State Truncation Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, 2021 DOI:10.1145/3410220.3460099

[8]	Refining Mean-field Approximations by Dynamic State Truncation ACM SIGMETRICS Performance Evaluation Review, 2021 DOI:10.1145/3543516.3460099

[9]	Refining Mean-field Approximations by Dynamic State Truncation Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2021 DOI:10.1145/3460092

[10]	Refining Mean-field Approximations by Dynamic State Truncation Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, 2021 DOI:10.1145/3410220.3460099

[11]	Refining Mean-field Approximations by Dynamic State Truncation Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2021 DOI:10.1145/3460092

[12]	Refining Mean-field Approximations by Dynamic State Truncation Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, 2021 DOI:10.1145/3410220.3460099

[13]	Refining Mean-field Approximations by Dynamic State Truncation ACM SIGMETRICS Performance Evaluation Review, 2021 DOI:10.1145/3543516.3460099

[14]	Slack reactants: A state-space truncation framework to estimate quantitative behavior of the chemical master equation The Journal of Chemical Physics, 2020 DOI:10.1063/5.0013457

[15]	Characterization of the conditional stationary distribution in Markov chains via systems of linear inequalities Advances in Applied Probability, 2020 DOI:10.1017/apr.2020.40

[16]	Slack reactants: A state-space truncation framework to estimate quantitative behavior of the chemical master equation The Journal of Chemical Physics, 2020 DOI:10.1063/5.0013457

[17]	Bounding the stationary distributions of the chemical master equation via mathematical programming The Journal of Chemical Physics, 2019 DOI:10.1063/1.5100670

[18]	A finite state projection method for steady-state sensitivity analysis of stochastic reaction networks The Journal of Chemical Physics, 2019 DOI:10.1063/1.5085271

[19]	A finite state projection method for steady-state sensitivity analysis of stochastic reaction networks The Journal of Chemical Physics, 2019 DOI:10.1063/1.5085271

[20]	Bounding the stationary distributions of the chemical master equation via mathematical programming The Journal of Chemical Physics, 2019 DOI:10.1063/1.5100670

[21]	ERROR BOUNDS FOR LAST-COLUMN-BLOCK-AUGMENTED TRUNCATIONS OF BLOCK-STRUCTURED MARKOV CHAINS Journal of the Operations Research Society of Japan, 2017 DOI:10.15807/jorsj.60.271

[22]	Continuous-time block-monotone Markov chains and their block-augmented truncations Linear Algebra and its Applications, 2017 DOI:10.1016/j.laa.2016.10.014

[23]	A finite state projection algorithm for the stationary solution of the chemical master equation The Journal of Chemical Physics, 2017 DOI:10.1063/1.5006484

[24]	A finite state projection algorithm for the stationary solution of the chemical master equation The Journal of Chemical Physics, 2017 DOI:10.1063/1.5006484

[25]	Error Bounds for Augmented Truncations of Discrete-Time Block-Monotone Markov Chains under Subgeometric Drift Conditions SIAM Journal on Matrix Analysis and Applications, 2016 DOI:10.1137/15M1024743

[26]	Perturbation analysis for continuous-time Markov chains Science China Mathematics, 2015 DOI:10.1007/s11425-015-5019-z

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