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Relevant bibliographies by topics / Controlled Markov chain

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Journal articles

Academic literature on the topic 'Controlled Markov chain'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Controlled Markov chain"

1

Ray, Anandaroop, David L. Alumbaugh, G. Michael Hoversten, and Kerry Key. "Robust and accelerated Bayesian inversion of marine controlled-source electromagnetic data using parallel tempering." GEOPHYSICS 78, no. 6 (2013): E271—E280. http://dx.doi.org/10.1190/geo2013-0128.1.

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Bayesian methods can quantify the model uncertainty that is inherent in inversion of highly nonlinear geophysical problems. In this approach, a model likelihood function based on knowledge of the data noise statistics is used to sample the posterior model distribution, which conveys information on the resolvability of the model parameters. Because these distributions are multidimensional and nonlinear, we used Markov chain Monte Carlo methods for highly efficient sampling. Because a single Markov chain can become stuck in a local probability mode, we run various randomized Markov chains indepe

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2

Lefebvre, Mario, and Moussa Kounta. "Discrete homing problems." Archives of Control Sciences 23, no. 1 (2013): 5–18. http://dx.doi.org/10.2478/v10170-011-0039-6.

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Abstract We consider the so-called homing problem for discrete-time Markov chains. The aim is to optimally control the Markov chain until it hits a given boundary. Depending on a parameter in the cost function, the optimizer either wants to maximize or minimize the time spent by the controlled process in the continuation region. Particular problems are considered and solved explicitly. Both the optimal control and the value function are obtained

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3

Andini, Enggartya, Sudarno Sudarno, and Rita Rahmawati. "PENERAPAN METODE PENGENDALIAN KUALITAS MEWMA BERDASARKAN ARL DENGAN PENDEKATAN RANTAI MARKOV (Studi Kasus: Batik Semarang 16, Meteseh)." Jurnal Gaussian 10, no. 1 (2021): 125–35. http://dx.doi.org/10.14710/j.gauss.v10i1.30939.

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An industrial company requires quality control to maintain quality consistency from the production results so that it is able to compete with other companies in the world market. In the industrial sector, most processes are influenced by more than one quality characteristic. One tool that can be used to control more than one quality characteristic is the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart. The graph is used to determine whether the process has been controlled or not, if the process is not yet controlled, the next analysis that can be used is to use the Ave

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4

CAI, KAI-YUAN, TSONG YUEH CHEN, YONG-CHAO LI, YUEN TAK YU, and LEI ZHAO. "ON THE ONLINE PARAMETER ESTIMATION PROBLEM IN ADAPTIVE SOFTWARE TESTING." International Journal of Software Engineering and Knowledge Engineering 18, no. 03 (2008): 357–81. http://dx.doi.org/10.1142/s0218194008003696.

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Software cybernetics is an emerging area that explores the interplay between software and control. The controlled Markov chain (CMC) approach to software testing supports the idea of software cybernetics by treating software testing as a control problem, where the software under test serves as a controlled object modeled by a controlled Markov chain and the software testing strategy serves as the corresponding controller. The software under test and the corresponding software testing strategy form a closed-loop feedback control system. The theory of controlled Markov chains is used to design a

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5

Li, Jinzhi, and Shixia Ma. "Pricing Options with Credit Risk in Markovian Regime-Switching Markets." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/621371.

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This paper investigates the valuation of European option with credit risk in a reduced form model when the stock price is driven by the so-called Markov-modulated jump-diffusion process, in which the arrival rate of rare events and the volatility rate of stock are controlled by a continuous-time Markov chain. We also assume that the interest rate and the default intensity follow the Vasicek models whose parameters are governed by the same Markov chain. We study the pricing of European option and present numerical illustrations.

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6

Dshalalow, Jewgeni. "On the multiserver queue with finite waiting room and controlled input." Advances in Applied Probability 17, no. 2 (1985): 408–23. http://dx.doi.org/10.2307/1427148.

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In this paper we study a multi-channel queueing model of type with N waiting places and a non-recurrent input flow dependent on queue length at the time of each arrival. The queue length is treated as a basic process. We first determine explicitly the limit distribution of the embedded Markov chain. Then, by introducing an auxiliary Markov process, we find a simple relationship between the limiting distribution of the Markov chain and the limiting distribution of the original process with continuous time parameter. Here we simultaneously combine two methods: solving the corresponding Kolmogoro

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7

Dshalalow, Jewgeni. "On the multiserver queue with finite waiting room and controlled input." Advances in Applied Probability 17, no. 02 (1985): 408–23. http://dx.doi.org/10.1017/s0001867800015044.

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In this paper we study a multi-channel queueing model of type with N waiting places and a non-recurrent input flow dependent on queue length at the time of each arrival. The queue length is treated as a basic process. We first determine explicitly the limit distribution of the embedded Markov chain. Then, by introducing an auxiliary Markov process, we find a simple relationship between the limiting distribution of the Markov chain and the limiting distribution of the original process with continuous time parameter. Here we simultaneously combine two methods: solving the corresponding Kolmogoro

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8

Attia, F. A. "The control of a finite dam with penalty cost function: Markov input rate." Journal of Applied Probability 24, no. 2 (1987): 457–65. http://dx.doi.org/10.2307/3214269.

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The long-run average cost per unit time of operating a finite dam controlled by a policy (Lam Yeh (1985)) is determined when the cumulative input process is the integral of a Markov chain. A penalty cost which accrues continuously at a rate g(X(t)), where g is a bounded measurable function of the content, is also introduced. An example where the input rate is a two-state Markov chain is considered in detail to illustrate the computations.

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9

Attia, F. A. "The control of a finite dam with penalty cost function: Markov input rate." Journal of Applied Probability 24, no. 02 (1987): 457–65. http://dx.doi.org/10.1017/s0021900200031090.

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Abstract:

The long-run average cost per unit time of operating a finite dam controlled by a policy (Lam Yeh (1985)) is determined when the cumulative input process is the integral of a Markov chain. A penalty cost which accrues continuously at a rate g(X(t)), where g is a bounded measurable function of the content, is also introduced. An example where the input rate is a two-state Markov chain is considered in detail to illustrate the computations.

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10

Fort, Gersende. "Central limit theorems for stochastic approximation with controlled Markov chain dynamics." ESAIM: Probability and Statistics 19 (2015): 60–80. http://dx.doi.org/10.1051/ps/2014013.

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