Journal articles: 'Markov noise'

1

Novak, Stephanie, and Lyman J. Fretwell. "Non‐Markov noise processes." Journal of the Acoustical Society of America 80, S1 (December 1986): S64. http://dx.doi.org/10.1121/1.2023904.

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Kinge, Sanjaykumar, B. Sheela Rani, and Mukul Sutaone. "Restored texture segmentation using Markov random fields." Mathematical Biosciences and Engineering 20, no. 6 (2023): 10063–89. http://dx.doi.org/10.3934/mbe.2023442.

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<abstract> <p>Texture segmentation plays a crucial role in the domain of image analysis and its recognition. Noise is inextricably linked to images, just like it is with every signal received by sensing, which has an impact on how well the segmentation process performs in general. Recent literature reveals that the research community has started recognizing the domain of noisy texture segmentation for its work towards solutions for the automated quality inspection of objects, decision support for biomedical images, facial expressions identification, retrieving image data from a huge dataset and many others. Motivated by the latest work on noisy textures, during our work being presented here, Brodatz and Prague texture images are contaminated with Gaussian and salt-n-pepper noise. A three-phase approach is developed for the segmentation of textures contaminated by noise. In the first phase, these contaminated images are restored using techniques with excellent performance as per the recent literature. In the remaining two phases, segmentation of the restored textures is carried out by a novel technique developed using Markov Random Fields (MRF) and objective customization of the Median Filter based on segmentation performance metrics. When the proposed approach is evaluated on Brodatz textures, an improvement of up to 16% segmentation accuracy for salt-n-pepper noise with 70% noise density and 15.1% accuracy for Gaussian noise (with a variance of 50) has been made in comparison with the benchmark approaches. On Prague textures, accuracy is improved by 4.08% for Gaussian noise (with variance 10) and by 2.47% for salt-n-pepper noise with 20% noise density. The approach in the present study can be applied to a diversified class of image analysis applications spanning a wide spectrum such as satellite images, medical images, industrial inspection, geo-informatics, etc.</p> </abstract>

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Zhou, Xiaozhen, Shanping Li, and Zhen Ye. "A Novel System Anomaly Prediction System Based on Belief Markov Model and Ensemble Classification." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/179390.

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Computer systems are becoming extremely complex, while system anomalies dramatically influence the availability and usability of systems. Online anomaly prediction is an important approach to manage imminent anomalies, and the high accuracy relies on precise system monitoring data. However, precise monitoring data is not easily achievable because of widespread noise. In this paper, we present a method which integrates an improved Evidential Markov model and ensemble classification to predict anomaly for systems with noise. Traditional Markov models use explicit state boundaries to build the Markov chain and then make prediction of different measurement metrics. A Problem arises when data comes with noise because even slight oscillation around the true value will lead to very different predictions. Evidential Markov chain method is able to deal with noisy data but is not suitable in complex data stream scenario. The Belief Markov chain that we propose has extended Evidential Markov chain and can cope with noisy data stream. This study further applies ensemble classification to identify system anomaly based on the predicted metrics. Extensive experiments on anomaly data collected from 66 metrics in PlanetLab have confirmed that our approach can achieve high prediction accuracy and time efficiency.

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Sanches, I. "Noise-compensated hidden Markov models." IEEE Transactions on Speech and Audio Processing 8, no. 5 (2000): 533–40. http://dx.doi.org/10.1109/89.861372.

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Chyan, Phie, and N. Tri Saptadi. "Pemulihan Citra Berbasis Metode Markov Random Field." JURIKOM (Jurnal Riset Komputer) 9, no. 2 (April 29, 2022): 218. http://dx.doi.org/10.30865/jurikom.v9i2.3966.

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Image processing and computer vision today are faced with increasing big data applications. Excessive collection of Image data sometimes can have bad quality due to errors at the time of acquisition or at the time of transmission, so for that problem the method is needed to perform image restoration. Image restoration is a process to make improvements to the image with the aim of obtaining a clean image from noise like the original image. Among the methods that can be used in image restoration, Markov Random Field (MRF) based on a probabilistic representation of image processing problems, namely maximizing the probability size calculated starting from the input data for all candidate solutions can provide a faster sub-optimal solution for image restoration. Based on the implementation this experiment conducted with the noisy test image, the MRF method was capable to improve the noisy image up to 96.75 percent close to the original image without noise

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Korolkiewicz, Małgorzata Wiktoria. "A Dependent Hidden Markov Model of Credit Quality." International Journal of Stochastic Analysis 2012 (August 13, 2012): 1–13. http://dx.doi.org/10.1155/2012/719237.

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We propose a dependent hidden Markov model of credit quality. We suppose that the "true" credit quality is not observed directly but only through noisy observations given by posted credit ratings. The model is formulated in discrete time with a Markov chain observed in martingale noise, where "noise" terms of the state and observation processes are possibly dependent. The model provides estimates for the state of the Markov chain governing the evolution of the credit rating process and the parameters of the model, where the latter are estimated using the EM algorithm. The dependent dynamics allow for the so-called "rating momentum" discussed in the credit literature and also provide a convenient test of independence between the state and observation dynamics.

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BORDONE, PAOLO, FABRIZIO BUSCEMI, and CLAUDIA BENEDETTI. "EFFECT OF MARKOV AND NON-MARKOV CLASSICAL NOISE ON ENTANGLEMENT DYNAMICS." Fluctuation and Noise Letters 11, no. 03 (September 2012): 1242003. http://dx.doi.org/10.1142/s0219477512420035.

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We analyze the effect of a classical noise into the entanglement dynamics between two particles, initially entangled, subject to continuous time quantum walks in a one-dimensional lattice. The noise is modeled by randomizing the transition amplitudes from one site to another. Both Markovian and non-Markovian environments are considered. For the Markov regime an exponential decay of the initial quantum correlation is found, while the loss of coherence of the quantum state increases monotonically with time up to a saturation value depending upon the degrees of freedom of the system. For the non-Markov regime the presence or absence of entanglement revival and entanglement sudden death phenomena is found or deduced depending on the peculiar characteristics of the noise. Our results indicate that the entanglement dynamics in the non-Markovian regime is affected by the persistence of the memory effects of the environment and by its intrinsic features.

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Borkar, Vivek S. "Stochastic approximation with ‘controlled Markov’ noise." Systems & Control Letters 55, no. 2 (February 2006): 139–45. http://dx.doi.org/10.1016/j.sysconle.2005.06.005.

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9

Godtliebsen, Fred. "Noise reduction using markov random fields." Journal of Magnetic Resonance (1969) 92, no. 1 (March 1991): 102–14. http://dx.doi.org/10.1016/0022-2364(91)90251-n.

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Alajaji, F., N. Phamdo, N. Farvardin, and T. E. Fuja. "Detection of binary Markov sources over channels with additive Markov noise." IEEE Transactions on Information Theory 42, no. 1 (1996): 230–39. http://dx.doi.org/10.1109/18.481793.

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SIEFERT, M., and J. PEINKE. "RECONSTRUCTION OF THE DETERMINISTIC DYNAMICS OF STOCHASTIC SYSTEMS." International Journal of Bifurcation and Chaos 14, no. 06 (June 2004): 2005–10. http://dx.doi.org/10.1142/s0218127404010436.

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We show that based on the mathematics of Markov processes and particularly based on the definition of Kramers–Moyal coefficients, it is possible to estimate the deterministic part of the dynamics for a broad class of nonlinear noisy systems. In particular, we show that for different kinds of noise perturbations, including non-Langevin force with finite correlation time and independent measurement noise, the deterministic part can be reconstructed.

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Kavcic, A., and J. M. F. Moura. "The Viterbi algorithm and Markov noise memory." IEEE Transactions on Information Theory 46, no. 1 (2000): 291–301. http://dx.doi.org/10.1109/18.817531.

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13

Jimenez-Aquino, J. I., and J. M. Sancho. "NLRT formalism for asymmetric dichotomous Markov noise." Journal of Physics A: Mathematical and General 25, no. 23 (December 7, 1992): 6179–86. http://dx.doi.org/10.1088/0305-4470/25/23/015.

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Kotler, Gregory, Valery Kontorovich, Vladimir Lyandres, and Sergey Primak. "On the Markov model of shot noise." Signal Processing 75, no. 1 (January 1999): 79–88. http://dx.doi.org/10.1016/s0165-1684(98)00226-6.

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15

Iskandar, R., and F. Alarid-Escudero. "PNS45 ADDING NOISE TO MARKOV COHORT MODELS." Value in Health 22 (November 2019): S770. http://dx.doi.org/10.1016/j.jval.2019.09.1947.

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Chvosta, Petr, and Peter Reineker. "Non-Markov noise in barrier-fluctuation model." Journal of Physics A: Mathematical and General 30, no. 10 (May 21, 1997): L307—L312. http://dx.doi.org/10.1088/0305-4470/30/10/002.

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Dombrovskii, Vladimir Valentinovich, and Tatiana Yurievna Pashinskaya. "Predictive control for markov jump systems with markov switching autoregressive multiplicative noise." Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitel'naya tekhnika i informatika, no. 44 (September 1, 2018): 4–9. http://dx.doi.org/10.17223/19988605/44/1.

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Yin, George, and Zhexin Wen. "TWO-TIME-SCALE REGIME-SWITCHING STOCHASTIC KOLGOMOROV SYSTEMS WITH WIDEBAND NOISES." Annals of the Academy of Romanian Scientists Series on Mathematics and Its Application 12, no. 1-2 (2020): 62–86. http://dx.doi.org/10.56082/annalsarscimath.2020.1-2.62.

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In our recent work, in lieu of using white noise, we examined Kolmogorov systems driven by wideband noise. Such systems naturally arise in statistical physics, biological and ecological systems, and many related fields. One of the motivations of our study is to treat more realistic models than the usually assumed stochastic differential equation models. The rationale is that a Brownian motion is an idealization used in a wide range of models, whereas wideband noise processes are much easier to be realized in the actual applications. This paper further investigates the case that in addition to the wideband noise process, there is a singularly perturbed Markov chain. The added Markov chain is used to model discrete events. Although it is a more realistic formulation, because of the non-Markovian formulation due to the wideband noise and the singularly perturbed Markov chain, the analysis is more difficult. Using weak convergence methods, we obtain a limit result. Then we provide several examples for the utility of our findings.

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Kennedy, J. E. "Noisy fluctuating clocks for Markov chains." Mathematical Proceedings of the Cambridge Philosophical Society 110, no. 1 (July 1991): 199–205. http://dx.doi.org/10.1017/s0305004100070250.

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AbstractIn this paper we concern ourselves with the Q-matrices of time-substituted Markov chains where the time-substitutions are based on fluctuating clocks with noise. The results of this paper follow on naturally from the analysis in Kennedy and Williams [3], in which the problems of characterization and of calculation of these Q-matrices were addressed. Here, we study the Q-matrices as functions of the variance σ2 of the noise.

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BRAUN, WOLFGANG, and BRUNO ECKHARDT. "STATISTICS OF SKIPPING EVENTS IN A NOISY THERMORECEPTOR." Fluctuation and Noise Letters 04, no. 01 (March 2004): L231—L236. http://dx.doi.org/10.1142/s0219477504001811.

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We analyze the statistics of skipping events in a deterministic and a noisy thermoreceptor within the model developed by Braun et al (Int. J. Bif. Chaos 8 881 (1998)). The statistics of skips can be captured by an intermittent map, which, for sufficient noise, can be approximated by a one-step Markov process with transition amplitudes that depend on the noise intensity. The theoretical and model results are in reasonable agreement with experimental data on cat coldreceptors.

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Lu, Chun. "Dynamical Behavior of Stochastic Markov Switching Hepatitis B Epidemic Model with Saturated Incidence Rate." Journal of Function Spaces 2022 (January 17, 2022): 1–8. http://dx.doi.org/10.1155/2022/5574983.

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The article researches a stochastic hepatitis B epidemic model with saturated incidence rate, which is perturbed by both white noise and colored noise. Firstly, we obtain a significant criterion R 0 S which relies on environmental noises. By means of Lyapunov function approach, we show that there is a stationary distribution if R 0 S > 1 . Its condition implies that when white noise is small, in the stochastic model, there exists a stochastic positive equilibrium state without changing the basic properties of its corresponding deterministic model. Secondly, we derive sufficient criteria for extinction of the disease. Finally, we propose a definition of the solution to an impulsive stochastic functional differential equation with Markovian switching (ISFDM).

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Zhu, Hong, Gui Xia Guan, and Min Hua Wu. "Dynamic Modeling and Evaluating of Moving Object Tracking with Correlated Measuring Noises." Applied Mechanics and Materials 121-126 (October 2011): 2666–70. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.2666.

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In moving object positioning and tracking, the GPS positioning error is colored noise with time correlations. According to the results of time sequential analyses, the model of the positioning error can be described as a AR(10) model. In order to decrease the computations and realize the real-time positioning and tracking of the moving objects, the AR(10) model is fit to be a 2-order Markov process. The positioning data act as the measurements, the system equations are built, in which the measuring noises have time-correlation. Subtracting the adjacent measurements, the correlated portions of measurements are eliminated, and the differences of the measurements are considered to be the new measurements. The new system equations are rebuilt and the recursive state evaluation algorithm is given, in which the measuring noise is white noise. It avoids higher-order matrix inverse calculations and fewer memories are needed. The simulation results show that the 2-order Markov process can effectively fit to the GPS error model and the precision of the moving object positioning is increased.

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Giuggioli, Luca, and Zohar Neu. "Fokker–Planck representations of non-Markov Langevin equations: application to delayed systems." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 377, no. 2153 (July 22, 2019): 20180131. http://dx.doi.org/10.1098/rsta.2018.0131.

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Noise and time delays, or history-dependent processes, play an integral part in many natural and man-made systems. The resulting interplay between random fluctuations and time non-locality are essential features of the emerging complex dynamics in non-Markov systems. While stochastic differential equations in the form of Langevin equations with additive noise for such systems exist, the corresponding probabilistic formalism is yet to be developed. Here we introduce such a framework via an infinite hierarchy of coupled Fokker–Planck equations for the n -time probability distribution. When the non-Markov Langevin equation is linear, we show how the hierarchy can be truncated at n = 2 by converting the time non-local Langevin equation to a time-local one with additive coloured noise. We compare the resulting Fokker–Planck equations to an earlier version, solve them analytically and analyse the temporal features of the probability distributions that would allow to distinguish between Markov and non-Markov features. This article is part of the theme issue ‘Nonlinear dynamics of delay systems’.

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Yaji, Vinayaka G., and Shalabh Bhatnagar. "Stochastic Recursive Inclusions in Two Timescales with Nonadditive Iterate-Dependent Markov Noise." Mathematics of Operations Research 45, no. 4 (November 2020): 1405–44. http://dx.doi.org/10.1287/moor.2019.1037.

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In this paper, we study the asymptotic behavior of a stochastic approximation scheme on two timescales with set-valued drift functions and in the presence of nonadditive iterate-dependent Markov noise. We show that the recursion on each timescale tracks the flow of a differential inclusion obtained by averaging the set-valued drift function in the recursion with respect to a set of measures accounting for both averaging with respect to the stationary distributions of the Markov noise terms and the interdependence between the two recursions on different timescales. The framework studied in this paper builds on a recent work by Ramaswamy and Bhatnagar, by allowing for the presence of nonadditive iterate-dependent Markov noise. As an application, we consider the problem of computing the optimum in a constrained convex optimization problem, where the objective function and the constraints are averaged with respect to the stationary distribution of an underlying Markov chain. Further, the proposed scheme neither requires the differentiability of the objective function nor the knowledge of the averaging measure.

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HERRMANN, S., and P. VALLOIS. "FROM PERSISTENT RANDOM WALK TO THE TELEGRAPH NOISE." Stochastics and Dynamics 10, no. 02 (June 2010): 161–96. http://dx.doi.org/10.1142/s0219493710002905.

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We study a family of memory-based persistent random walks and we prove weak convergences after space-time rescaling. The limit processes are not only Brownian motions with drift. We have obtained a continuous but non-Markov process (Zt) which can be easily expressed in terms of a counting process (Nt). In a particular case the counting process is a Poisson process, and (Zt) permits to represent the solution of the telegraph equation. We study in detail the Markov process ((Zt, Nt); t ≥ 0).

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BENEDETTI, CLAUDIA, FABRIZIO BUSCEMI, PAOLO BORDONE, and MATTEO G. A. PARIS. "EFFECTS OF CLASSICAL ENVIRONMENTAL NOISE ON ENTANGLEMENT AND QUANTUM DISCORD DYNAMICS." International Journal of Quantum Information 10, no. 08 (December 2012): 1241005. http://dx.doi.org/10.1142/s0219749912410055.

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We address the effect of classical noise on the dynamics of quantum correlations, entanglement and quantum discord (QD), of two non-interacting qubits initially prepared in a Bell state. The effect of noise is modeled by randomizing the single-qubit transition amplitudes. We address both static and dynamic environmental noise corresponding to interaction with separate and common baths in either Markovian and non-Markovian regimes. In the Markov regime, a monotone decay of the quantum correlations is found, whereas for non-Markovian noise sudden death and revival phenomena may occur, depending on the characteristics of the noise. Entanglement and QD show the same qualitative behavior for all kind of noises considered. On the other hand, we find that separate and common environments may play opposite roles in preserving quantum correlations, depending on the noise regime considered.

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Arias-Castro, Ery, Sébastien Bubeck, Gábor Lugosi, and Nicolas Verzelen. "Detecting Markov random fields hidden in white noise." Bernoulli 24, no. 4B (November 2018): 3628–56. http://dx.doi.org/10.3150/17-bej973.

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Chigansky, P. "On Filtering of Markov Chains in Strong Noise." IEEE Transactions on Information Theory 52, no. 9 (September 2006): 4267–72. http://dx.doi.org/10.1109/tit.2006.880042.

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Maras, A. M. "Locally optimum Bayes detection in ergodic Markov noise." IEEE Transactions on Information Theory 40, no. 1 (1994): 41–55. http://dx.doi.org/10.1109/18.272454.

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Chvosta, Petr, and Peter Reineker. "Dynamics under the influence of semi-Markov noise." Physica A: Statistical Mechanics and its Applications 268, no. 1-2 (June 1999): 103–20. http://dx.doi.org/10.1016/s0378-4371(99)00021-7.

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Borisov, Andrey, and Andrey Gorshenin. "Identification of Continuous-Discrete Hidden Markov Models with Multiplicative Observation Noise." Mathematics 10, no. 17 (August 25, 2022): 3062. http://dx.doi.org/10.3390/math10173062.

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The paper aims to identify hidden Markov model parameters. The unobservable state represents a finite-state Markov jump process. The observations contain Wiener noise with state-dependent intensity. The identified parameters include the transition intensity matrix of the system state, conditional drift and diffusion coefficients in the observations. We propose an iterative identification algorithm based on the fixed-interval smoothing of the Markov state. Using the calculated state estimates, we restore all required system parameters. The paper contains a detailed description of the numerical schemes of state estimation and parameter identification. The comprehensive numerical study confirms the high precision of the proposed identification estimates.

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Gellert, Arpad, Remus Brad, Daniel Morariu, and Mihai Neghina. "Filtering Random Valued Impulse Noise from Grayscale Images through Support Vector Machine and Markov Chain." International Journal of Advanced Statistics and IT&C for Economics and Life Sciences 11, no. 1 (December 1, 2021): 70–84. http://dx.doi.org/10.2478/ijasitels-2021-0004.

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Abstract This paper presents a context-based filter to denoise grayscale images affected by random valued impulse noise. A support vector machine classifier is used for noise detection and two Markov filter variants are evaluated for their denoising capacity. The classifier needs to be trained on a set of training images. The experiments performed on another set of test images have shown that the support vector machine with the radial basis function kernel combined with the Markov+ filter is the best configuration, providing the highest noise detection accuracy. Our filter was compared with existing denoising methods, it being better on some images and comparable with them on others.

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Stathopoulos, Vassilios, and Mark A. Girolami. "Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1984 (February 13, 2013): 20110541. http://dx.doi.org/10.1098/rsta.2011.0541.

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Bayesian analysis for Markov jump processes (MJPs) is a non-trivial and challenging problem. Although exact inference is theoretically possible, it is computationally demanding, thus its applicability is limited to a small class of problems. In this paper, we describe the application of Riemann manifold Markov chain Monte Carlo (MCMC) methods using an approximation to the likelihood of the MJP that is valid when the system modelled is near its thermodynamic limit. The proposed approach is both statistically and computationally efficient whereas the convergence rate and mixing of the chains allow for fast MCMC inference. The methodology is evaluated using numerical simulations on two problems from chemical kinetics and one from systems biology.

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Suryawan, Herry Pribawanto. "SOME BASIC PROPERTIES OF THE NOISE REINFORCED BROWNIAN MOTION." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 2 (June 1, 2022): 363–70. http://dx.doi.org/10.30598/barekengvol16iss2pp363-370.

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Noise reinforced Brownian motion appears as the universal limit of the step reinforced random walk. This article aims to study some basic properties of the noise reinforced Brownian motion. As main results, we prove integral representation, series expansion, Markov property, and martingale property of the noise reinforced Brownian motion.

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Møller, Jesper. "Shot noise Cox processes." Advances in Applied Probability 35, no. 3 (September 2003): 614–40. http://dx.doi.org/10.1239/aap/1059486821.

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Shot noise Cox processes constitute a large class of Cox and Poisson cluster processes in ℝd, including Neyman-Scott, Poisson-gamma and shot noise G Cox processes. It is demonstrated that, due to the structure of such models, a number of useful and general results can easily be established. The focus is on the probabilistic aspects with a view to statistical applications, particularly results for summary statistics, reduced Palm distributions, simulation with or without edge effects, conditional simulation of the intensity function and local and spatial Markov properties.

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Møller, Jesper. "Shot noise Cox processes." Advances in Applied Probability 35, no. 03 (September 2003): 614–40. http://dx.doi.org/10.1017/s0001867800012465.

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Shot noise Cox processes constitute a large class of Cox and Poisson cluster processes in ℝd, including Neyman-Scott, Poisson-gamma and shot noise G Cox processes. It is demonstrated that, due to the structure of such models, a number of useful and general results can easily be established. The focus is on the probabilistic aspects with a view to statistical applications, particularly results for summary statistics, reduced Palm distributions, simulation with or without edge effects, conditional simulation of the intensity function and local and spatial Markov properties.

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Li, Dianqiang, and Tao Li. "The cooperatability of the first-order multi-agent systems consisting of a leader and a follower with multiplicative noises under Markov switching topologies." Intelligence & Robotics 3, no. 2 (June 28, 2023): 213–21. http://dx.doi.org/10.20517/ir.2023.13.

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We investigate the cooperatability of the first-order leader-following multi-agent systems consisting of a leader and a follower with multiplicative noises under Markov switching topologies. Each agent exhibits first-order linear dynamics, and there are multiplicative noises along with information exchange among the agents. What is more, the communication topologies are Markov switching topologies. By utilizing the stability theory of the stochastic differential equations with Markovian switching and the Markov chain theory, we establish the necessary and sufficient conditions for the cooperatability of the leader-following multi-agent systems. The conditions are outlined below: (ⅰ) The product of the system parameter and the square of multiplicative noise intensities should be less than 1/2; (ⅱ) The transition rate from the unconnected graph to the connected graph should be twice the system parameter; (ⅲ) The transition rate from the connected graph to the unconnected graph should be less than a constant that is related to the system parameter, the intensities of multiplicative noises, and the transition rate from the unconnected graph to the connected graph. Finally, the effectiveness of our control strategy is demonstrated by the population growth systems.

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Ma, Hongji, and Yang Wang. "Full Information H2 Control of Borel-Measurable Markov Jump Systems with Multiplicative Noises." Mathematics 10, no. 1 (December 23, 2021): 37. http://dx.doi.org/10.3390/math10010037.

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This paper addresses an H2 optimal control problem for a class of discrete-time stochastic systems with Markov jump parameter and multiplicative noises. The involved Markov jump parameter is a uniform ergodic Markov chain taking values in a Borel-measurable set. In the presence of exogenous white noise disturbance, Gramian characterization is derived for the H2 norm, which quantifies the stationary variance of output response for the considered systems. Moreover, under the condition that full information of the system state is accessible to measurement, an H2 dynamic optimal control problem is shown to be solved by a zero-order stabilizing feedback controller, which can be represented in terms of the stabilizing solution to a set of coupled stochastic algebraic Riccati equations. Finally, an iterative algorithm is provided to get the approximate solution of the obtained Riccati equations, and a numerical example illustrates the effectiveness of the proposed algorithm.

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BENA, IOANA. "DICHOTOMOUS MARKOV NOISE: EXACT RESULTS FOR OUT-OF-EQUILIBRIUM SYSTEMS." International Journal of Modern Physics B 20, no. 20 (August 10, 2006): 2825–88. http://dx.doi.org/10.1142/s0217979206034881.

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Nonequilibrium systems driven by additive or multiplicative dichotomous Markov noise appear in a wide variety of physical and mathematical models. We review here some prototypical examples, with an emphasis on analytically-solvable situations. In particular, it has escaped attention till recently that the standard results for the long-time properties of such systems cannot be applied when unstable fixed points are crossed in the asymptotic regime. We show how calculations have to be modified to deal with these cases and present a few relevant applications — the hypersensitive transport, the rocking ratchet, and the stochastic Stokes' drift. These results reinforce the impression that dichotomous noise can be put on par with Gaussian white noise as far as obtaining analytical results is concerned. They convincingly illustrate the interplay between noise and nonlinearity in generating nontrivial behaviors of nonequilibrium systems and point to various practical applications.

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Sun, Fengjie, and Chenkai Zhao. "Research and Modeling of Photovoltaic Array Channel Noise Characteristics." Energies 12, no. 7 (April 1, 2019): 1257. http://dx.doi.org/10.3390/en12071257.

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The photovoltaic array can be used as a medium for carrier communication to realize monitoring of photovoltaic components. Photovoltaic array channel noise, especially the pulse-type noise therein, seriously interferes carrier communication, so it is necessary to grasp the characteristics of the photovoltaic array channel noise. Photovoltaic array channel noise modeling is a key process when conducting anti-noise immunity tests of monitoring equipment. Based on the time-domain waveform of photovoltaic series channel noise which is measured in a photovoltaic power station, this paper proposes a photovoltaic array noise modeling method of Wavelet Peak-Type Markov chain, and studies the influence on modeling accuracy when different mother wavelets are adopted for modeling. From the simulation results, root mean square errors of the predicted output for Haar, Biorthogonal and Daubechies wavelet-based function modeling case are 0.9614 V, 1.4915 V and 0.7928 V, respectively, validating that Daubechies wavelet-based function is the best wavelet-based function of modeling. In the case that the peak of original noise reaches 20 V, the predicted mean absolute error of this model is only 0.4926 V, which not only verifies the applicability of the Wavelet Peak-Type Markov chain model to the photovoltaic array channel noise, but also verifies the applicability to the pulse-type noise.

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LIU, PEIDONG, and YAN ZHENG. "MARKOV CHAIN PERTURBATIONS OF A CLASS OF PARTIALLY EXPANDING ATTRACTORS." Stochastics and Dynamics 06, no. 03 (September 2006): 341–54. http://dx.doi.org/10.1142/s0219493706001761.

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In this paper Markov chain perturbations of a class of partially expanding attractors of a diffeomorphism are considered. We show that, under some regularity conditions on the transition probabilities, the zero-noise limits of stationary measures of the Markov chains are Sinai–Ruelle–Bowen measures of the diffeomorphism on the attractors.

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BENA, I., R. KAWAI, C. VAN DEN BROECK, and KATJA LINDENBERG. "STOKES' DRIFT AND HYPERSENSITIVE RESPONSE WITH DICHOTOMOUS MARKOV NOISE." Fluctuation and Noise Letters 05, no. 03 (September 2005): L397—L415. http://dx.doi.org/10.1142/s0219477505002835.

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Stochastic Stokes' drift and hypersensitive transport driven by dichotomous noise are theoretically investigated. Explicit mathematical expressions for the asymptotic probability density and drift velocity are derived including the situation in which particles cross unstable fixed points. The results are confirmed by numerical simulations.

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Aron, Camille, Daniel G. Barci, Leticia F. Cugliandolo, Zochil González Arenas, and Gustavo S. Lozano. "Dynamical symmetries of Markov processes with multiplicative white noise." Journal of Statistical Mechanics: Theory and Experiment 2016, no. 5 (May 19, 2016): 053207. http://dx.doi.org/10.1088/1742-5468/2016/05/053207.

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Kokkinos, Evangelos A., and Andreas M. Maras. "Narrowband incoherent threshold detection in non-additive Markov noise." Signal Processing 72, no. 1 (January 1999): 39–45. http://dx.doi.org/10.1016/s0165-1684(98)00163-7.

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Stelzer, Robert. "Multivariate Markov-switching ARMA processes with regularly varying noise." Journal of Multivariate Analysis 99, no. 6 (July 2008): 1177–90. http://dx.doi.org/10.1016/j.jmva.2007.07.001.

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Zhao, Zijin, Lingyang Chu, Dacheng Tao, and Jian Pei. "Classification with label noise: a Markov chain sampling framework." Data Mining and Knowledge Discovery 33, no. 5 (October 6, 2018): 1468–504. http://dx.doi.org/10.1007/s10618-018-0592-8.

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Cui, Xiaodong, Mohamed Afify, Yuqing Gao, and Bowen Zhou. "Stereo hidden Markov modeling for noise robust speech recognition." Computer Speech & Language 27, no. 2 (February 2013): 407–19. http://dx.doi.org/10.1016/j.csl.2011.08.002.

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Maire, Florian, and Pierre Vandekerkhove. "Markov Kernels Local Aggregation for Noise Vanishing Distribution Sampling." SIAM Journal on Mathematics of Data Science 4, no. 4 (December 20, 2022): 1293–319. http://dx.doi.org/10.1137/22m1469626.

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Tseng, Shu-Ming, Yung-Chung Wang, Cheng-Wei Hu, and Tseng-Chun Lee. "Performance Analysis of CDMA/ALOHA Networks in Memory Impulse Channels." Mathematical Problems in Engineering 2018 (June 7, 2018): 1–8. http://dx.doi.org/10.1155/2018/9373468.

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Previous CDMA/ALOHA network performance analysis papers do not consider memory impulse channels. The Markov-Gaussian model can characterize the bursty nature of the memory impulsive noise in some wireless channels and power line communication (PLC) channels. In this paper, we propose the use of the Markov-Gaussian model in the throughput analysis of CDMA/ALOHA networks. The state transition diagram for the throughput analysis has one extra dimension to represent if the impulse noise is present or not. We derive the exact throughput analysis in memory impulse channels with and without channel load sensing protocol (CLSP). The analytic results demonstrate that if the level of the impulsive noise is higher, performance degradation is more and vice versa. Based on the analytic results, we also find the optimal CLSP threshold alpha for various impulse noise conditions. The analytic results of the throughput performance are all matched by the simulation results.

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Midoh, Yoshihiro, and Koji Nakamae. "Accuracy improvement of phase estimation in electron holography using noise reduction methods." Microscopy 69, no. 2 (January 15, 2020): 123–31. http://dx.doi.org/10.1093/jmicro/dfz115.

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Abstract We try to improve the limit of the phase estimation of the interference fringe at low electron dose levels in electron holography by a noise reduction method. In this paper, we focus on unsupervised approaches to apply it to electron beam-sensitive and unknown samples and describe an overview of denoising methods used widely in image processing, such as wiener filter, total variation denoising, nonlocal mean filters and wavelet thresholding. We compare the wavelet hidden Markov model (WHMM) denoising that we have studied so far with the other conventional noise reduction methods. We evaluate the denoise performance of each method using the peak signal-to-noise ratio between noise-free and the target holograms (noisy or denoised holograms) and the root mean-square error (RMSE) between the true phase of the fringe and the measured phase by the discrete Fourier transform phase estimator. We show the denoised holograms for simulation and experimental data by using each noise reduction method and then discuss evaluation indexes obtained from these denoised holograms. From experimental results, it can be seen that the WHMM denoising can reduce the RMSE of fringe phase to about 1/4.5 for noisy simulation holograms and it has stable and good performance for noise reduction of observed holograms with various image qualities.

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Journal articles on the topic 'Markov noise'

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