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Articoli di riviste sul tema "Random processes"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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1

Poliarus, Oleksandr, Andrii Lebedynskyi, Yevhenii Chepusenko, and Nina Lyubymova. "Visualization method for multidimentional random processes." Measuring Equipment and Metrology 84, no. 1 (2023): 5–10. http://dx.doi.org/10.23939/istcmtm2023.01.005.

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The article proposes a method for visualizing multidimensional random process realizations using the example of the concentrations of harmful gases emitted into the atmosphere from a thermal power plant. The method is based on the transformation of gas concentration values in one point of multidimensional space at the same time into a two-dimensional curve, which is described by the sum of products of normalized concentrations by orthogonal Legendre functions of the corresponding order. The combination of such curves on a two-dimensional plane at discrete times creates a characteristic image that can be used to visually detect features of gas concentrations over time by a human operator.

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2

Alexander, Kenneth S., and Steven A. Kalikow. "Random Stationary Processes." Annals of Probability 20, no. 3 (July 1992): 1174–98. http://dx.doi.org/10.1214/aop/1176989685.

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3

ROBALEWSKA, H. D., and N. C. WORMALD. "Random Star Processes." Combinatorics, Probability and Computing 9, no. 1 (January 2000): 33–43. http://dx.doi.org/10.1017/s096354839900406x.

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4

Aitken, G. J. M. "Illustrating Random Processes with Random Phase Modulation." International Journal of Electrical Engineering & Education 23, no. 2 (April 1986): 151–58. http://dx.doi.org/10.1177/002072098602300209.

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Randomly phase-modulated cosines are a source of examples for illustrating the topics of variance, autocorrelation, conditional probability and filtering. Mathematical manipulations are neither difficult nor tedious despite the non-linear relationship between measured quantities and the phase noise. The basic mathematical framework is presented in the context of examples which include synchronous detection in the presence of phase perturbations.

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5

Lyashenko, N. N. "Graphs of Random Processes as Random Sets." Theory of Probability & Its Applications 31, no. 1 (March 1987): 72–80. http://dx.doi.org/10.1137/1131006.

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6

Applebaum, David, Geoffrey Grimmett, David Stirzaker, Marek Capiński, Thomas Zastawniak, and Marek Capinski. "Probability and Random Processes." Mathematical Gazette 86, no. 505 (March 2002): 185. http://dx.doi.org/10.2307/3621637.

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7

Foutz, Robert V., G. R. Grimmett, and D. R. Stirzaker. "Probability and Random Processes." Journal of the American Statistical Association 88, no. 424 (December 1993): 1475. http://dx.doi.org/10.2307/2291308.

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8

Stoyanov, Jordan. "Probability and Random Processes." Journal of the Royal Statistical Society: Series A (Statistics in Society) 170, no. 4 (October 2007): 1183–84. http://dx.doi.org/10.1111/j.1467-985x.2007.00506_12.x.

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9

Meyer, Mary C., and Donald G. Childers. "Probability and Random Processes." Journal of the American Statistical Association 94, no. 447 (September 1999): 988. http://dx.doi.org/10.2307/2670024.

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10

Esmaili, Ali. "Probability and Random Processes." Technometrics 47, no. 3 (August 2005): 375. http://dx.doi.org/10.1198/tech.2005.s294.

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11

Fotopoulos, Stergios B. "Probability and Random Processes." Technometrics 49, no. 3 (August 2007): 365. http://dx.doi.org/10.1198/tech.2007.s516.

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12

Galicin, Volodymir, Dmytro Zhuk, and Anna Petrychenko. "Random processes in meteorology." Modeling and Information Systems in Economics, no. 102 (December 21, 2022): 49–67. http://dx.doi.org/10.33111/mise.102.5.

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13

Krishnan, V., and S. Lakshmivarahan. "Probability and Random Processes." IIE Transactions 40, no. 2 (November 23, 2007): 160. http://dx.doi.org/10.1080/07408170701623260.

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14

Stirzaker, David. "PROCESSES WITH RANDOM REGULATION." Probability in the Engineering and Informational Sciences 21, no. 1 (December 15, 2006): 1–17. http://dx.doi.org/10.1017/s0269964807070015.

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Abstract (sommario):

We consider a class of stochastic models for systems subject to random regulation. We derive expressions for the distribution of the intervals between regulating instants and for the transient and equilibrium properties of the process. Some of these are evaluated explicitly for some models of interest.

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15

Thompson, W. A., G. R. Grimmett, and D. R. Stirzaker. "Probability and Random Processes." Journal of the American Statistical Association 80, no. 391 (September 1985): 788. http://dx.doi.org/10.2307/2288525.

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16

Clifford, Peter, and David Stirzaker. "History-dependent random processes." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464, no. 2093 (February 5, 2008): 1105–24. http://dx.doi.org/10.1098/rspa.2007.0291.

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Ulam has defined a history-dependent random sequence by the recursion X n +1 = X n + X U ( n ) , where ( U ( n ); n ≥1) is a sequence of independent random variables with U ( n ) uniformly distributed on {1, …, n } and X 1 =1. We introduce a new class of continuous-time history-dependent random processes regulated by Poisson processes. The simplest of these, a univariate process regulated by a homogeneous Poisson process, replicates in continuous time the essential properties of Ulam's sequence, and greatly facilitates its analysis. We consider several generalizations and extensions of this, including bivariate and multivariate coupled history-dependent processes, and cases when the dependence on the past is not uniform. The analysis of the discrete-time formulations of these models would be at the very least an extremely formidable project, but we determine the asymptotic growth rates of their means and higher moments with relative ease.

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17

Pishel', R., and A. A. Yantsevich. "Dilations of random processes." Journal of Soviet Mathematics 48, no. 5 (February 1990): 566–70. http://dx.doi.org/10.1007/bf01095626.

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18

Horowitz, J. "Measure-valued random processes." Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete 70, no. 2 (August 1985): 213–36. http://dx.doi.org/10.1007/bf02451429.

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19

Han, Lengyi, W. John Braun, and Jason Loeppky. "Random coefficient minification processes." Statistical Papers 61, no. 4 (April 23, 2018): 1741–62. http://dx.doi.org/10.1007/s00362-018-1000-6.

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20

Rota, Gian-Carlo. "Stationary random processes associated with point processes." Advances in Mathematics 57, no. 2 (August 1985): 208. http://dx.doi.org/10.1016/0001-8708(85)90061-1.

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21

Khimenko, V. I. "Random processes with random transitions between stable states." Information and Control Systems, no. 3 (June 21, 2019): 82–93. http://dx.doi.org/10.31799/1684-8853-2019-3-82-93.

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Introduction: Studying random processes with several stable states and random transitions between them is important because it opens a wide range of practical problems. The detailed information structure is not studied well enough, and there is no unified approach to the description and probabilistic analysis of such processes.Purpose: Studying the main probabilistic characteristics of random processes with two stable states, and probabilistic analysis of control over chaotic transitions under various control actions.Results: We show the ways to represent and preliminarily analyze random processes with two stable states on the phase plane and in the pseudophase space. A general probabilistic model for the processes in question is proposed in the form of a two-component probabilistic «mixture» of distributions. A probabilistic analysis was carried out for the principles of control over random transitions between different states. We have defined the basic probabilistic characteristics for the processes in a management action with a variety of spectral-correlation properties and a changeable threshold for random transitions. The Poisson model of a random transition flow is analyzed with an example of «high» threshold levels.Practical relevance: The methods of visual, qualitative and analytical research in studying dynamic systems with several stable states can be combined. The proposed probabilistic models, regardless of the physical nature of the processes under consideration, can be used in problems of probabilistic analysis, control over probabilistic structure of random transitions, and simulation of physical, technical or biological systems with random switching.

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22

Filip, Silviu, Aurya Javeed, and Lloyd N. Trefethen. "Smooth Random Functions, Random ODEs, and Gaussian Processes." SIAM Review 61, no. 1 (January 2019): 185–205. http://dx.doi.org/10.1137/17m1161853.

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23

Wormald, Nicholas C. "Differential Equations for Random Processes and Random Graphs." Annals of Applied Probability 5, no. 4 (November 1995): 1217–35. http://dx.doi.org/10.1214/aoap/1177004612.

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24

Wells, Martin T. "Statistics of Random Processes I: General Theory, Statistics of Random Processes II: Applications." Journal of the American Statistical Association 96, no. 456 (December 2001): 1526–27. http://dx.doi.org/10.1198/jasa.2001.s428.

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25

FRYZ, Mykhailo, and Bogdana MLYNKO. "DISCRETE-TIME CONDITIONAL LINEAR RANDOM PROCESSES AND THEIR PROPERTIES." Herald of Khmelnytskyi National University. Technical sciences 309, no. 3 (May 26, 2022): 7–12. http://dx.doi.org/10.31891/2307-5732-2022-309-3-7-12.

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Continuous-time conditional linear random process is represented as a stochastic integral of a random kernel driven by a process with independent increments. Such processes are used in the problems of mathematical modelling, computer simulation, and processing of stochastic signals, the physical nature of which generates them to be represented as the sum of many random impulses that occur at Poisson moments. Impulses are stochastically dependent functions, in contrast to another well-known mathematical model which is a linear random process, that has a similar structure but is represented as the sum of a large amount of independent random impulses that occur at Poisson moments of time. The application areas of these models are mathematical modelling, computer simulation, and processing of electroencephalographic signals, cardio signals, resource consumption processes (such as electricity consumption, water consumption, gas consumption), radar signals, etc. A discrete-time conditional linear random process has been defined in the paper, the relationships with corresponding continuous-time model has been shown. According to the given definition the discrete-time conditional linear random process can be considered as an output of linear digital filter with random parameters on the input of the white noise which is infinitely divisible distributed. Moment functions of first and second order have been analyzed. In particular, the expressions for mathematical expectation, variance and covariance function have been obtained. The results can be utilized to study the probabilistic characteristics of the investigated information stochastic signals, which will depend on the properties of the corresponding kernel and white noise. In particular, the conditions for the process to be wide-sense stationary have been represented.

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26

Applebaum, Dave, G. Samorodnitsky, and M. S. Taqqu. "Stable Non-Gaussian Random Processes." Mathematical Gazette 79, no. 486 (November 1995): 625. http://dx.doi.org/10.2307/3618123.

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27

Peled, Ron, Vladas Sidoravicius, and Alexandre Stauffer. "Strongly Correlated Random Interacting Processes." Oberwolfach Reports 15, no. 1 (January 4, 2019): 187–253. http://dx.doi.org/10.4171/owr/2018/4.

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28

Панчева, Елизавета И., Elisaveta I. Pancheva, Ekaterina T. Kolkovska, Ekaterina T. Kolkovska, Pavlina Kalcheva Jordanova, and Pavlina Kalcheva Jordanova. "Random time-changed extremal processes." Teoriya Veroyatnostei i ee Primeneniya 51, no. 4 (2006): 752–72. http://dx.doi.org/10.4213/tvp23.

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29

LI, ZHIMING, QIN WANG, and YUANFANG WU. "WAVELET ANALYSIS FOR RANDOM PROCESSES." Modern Physics Letters A 16, no. 09 (March 21, 2001): 583–88. http://dx.doi.org/10.1142/s0217732301003620.

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The role of wavelet transformation in the study of random processes is investigated. It is shown that wavelet transformation does not change the scaling index of random multiplicative cascade process. On the other hand, for pure random process, wavelet transformation is able to suppress the trivial fluctuations, coming from probability conservation, which will show an apparent increase in moments with the diminishing of bin size.

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30

Vanhoff, Barry, and Steve Elgar. "Simulating Quadratically Nonlinear Random Processes." International Journal of Bifurcation and Chaos 07, no. 06 (June 1997): 1367–74. http://dx.doi.org/10.1142/s0218127497001084.

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A technique to generate realizations of quadratically nonlinear non-Gaussian time series with a desired ("target") power spectrum and bispectrum is presented. Specifically, by generating a Gaussian time series (using amplitude information from the target power spectrum and random phases) and passing it through a quadratic filter (that uses phase information from the target bispectrum), a realization of a quadratically nonlinear random process with a specified power spectrum and bispectrum can be produced. Second- and third-order statistics from many realizations of simulated nonlinear time series compare well to those from the original time series providing the target power spectrum and bispectrum, with deviations consistent with theory. The simulation technique is shown to simulate accurately ocean waves in shallow water, which are well known to be quadratically nonlinear.

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31

Ayache, A., and M. S. Taqqu. "Multifractional processes with random exponent." Publicacions Matemàtiques 49 (July 1, 2005): 459–86. http://dx.doi.org/10.5565/publmat_49205_11.

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32

Lavielle, M. "Optimal segmentation of random processes." IEEE Transactions on Signal Processing 46, no. 5 (May 1998): 1365–73. http://dx.doi.org/10.1109/78.668798.

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33

Pancheva, E. I., E. T. Kolkovska, and P. K. Jordanova. "Random Time-Changed Extremal Processes." Theory of Probability & Its Applications 51, no. 4 (January 2007): 645–62. http://dx.doi.org/10.1137/s0040585x97982694.

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34

Rothmann, Mark D., and Hammou El Barmi. "Stochastic processes involving random deletion." Journal of Applied Probability 38, no. 1 (March 2001): 95–107. http://dx.doi.org/10.1239/jap/996986646.

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Abstract (sommario):

We consider a system where units having magnitudes arrive according to a nonhomogeneous Poisson process, remain there for a random period and then depart. Eventually, at any point in time only a portion of those units which have entered the system remain. Of interest are the finite time properties and limiting behaviors of the distribution of magnitudes among the units present in the system and among those which have departed from the system. We will derive limiting results for the empirical distribution of magnitudes among the active (departed) units. These results are also shown to extend to systems having stages or steps through which units must proceed. Examples are given to illustrate these results.

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35

Saporta, Benoîte de, Anne Gégout-Petit, and Laurence Marsalle. "Random coefficients bifurcating autoregressive processes." ESAIM: Probability and Statistics 18 (2014): 365–99. http://dx.doi.org/10.1051/ps/2013042.

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36

Iqbal, Amer, Babar A. Qureshi, Khurram Shabbir, and Muhammad A. Shehper. "Brane webs and random processes." International Journal of Modern Physics A 30, no. 33 (November 26, 2015): 1550202. http://dx.doi.org/10.1142/s0217751x15502024.

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We study (p, q) 5-brane webs dual to certain N M5-brane configurations and show that the partition function of these brane webs gives rise to cylindric Schur process with period N. This generalizes the previously studied case of period 1. We also show that open string amplitudes corresponding to these brane webs are captured by the generating function of cylindric plane partitions with profile determined by the boundary conditions imposed on the open string amplitudes.

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37

Stadje, W., and S. Zacks. "Telegraph processes with random velocities." Journal of Applied Probability 41, no. 3 (September 2004): 665–78. http://dx.doi.org/10.1239/jap/1091543417.

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We study a one-dimensional telegraph process (Mt)t≥0 describing the position of a particle moving at constant speed between Poisson times at which new velocities are chosen randomly. The exact distribution of Mt and its first two moments are derived. We characterize the level hitting times of Mt in terms of integro-differential equations which can be solved in special cases.

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38

Szász, Domokos. "Random Walks and Lorentz Processes." Entropy 26, no. 11 (October 25, 2024): 908. http://dx.doi.org/10.3390/e26110908.

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Random walks and Lorentz processes serve as fundamental models for Brownian motion. The study of random walks is a favorite object of probability theory, whereas that of Lorentz processes belongs to the theory of hyperbolic dynamical systems. Here we first present an example where the method based on the probabilistic approach led to new results for the Lorentz process: concretely, the recurrence of the planar periodic Lorentz process with a finite horizon. Afterwards, an unsolved problem—related to a 1981 question of Sinai on locally perturbed periodic Lorentz processes—is formulated as an analogous problem in the language of random walks.

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39

Howroyd, Douglas C., and Han Yu. "Assouad Dimension of Random Processes." Proceedings of the Edinburgh Mathematical Society 62, no. 1 (November 16, 2018): 281–90. http://dx.doi.org/10.1017/s0013091518000433.

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AbstractIn this paper we study the Assouad dimension of graphs of certain Lévy processes and functions defined by stochastic integrals. We do this by introducing a convenient condition which guarantees a graph to have full Assouad dimension and then show that graphs of our studied processes satisfy this condition.

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40

Zubov, Vladimir I. "Random Variables and Stochastic Processes." IFAC Proceedings Volumes 33, no. 16 (July 2000): 403–14. http://dx.doi.org/10.1016/s1474-6670(17)39666-0.

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41

Corte, Aurelio La. "Generation of crosscorrelated random processes." Signal Processing 79, no. 3 (December 1999): 223–34. http://dx.doi.org/10.1016/s0165-1684(99)00097-3.

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42

Kingman, J. F. C. "Random dissections and branching processes." Mathematical Proceedings of the Cambridge Philosophical Society 104, no. 1 (July 1988): 147–51. http://dx.doi.org/10.1017/s0305004100065324.

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For a time in the mid-1970s probabilists were tantalized by a seemingly simple problem posed by Araki and Kakutani[3]. An interval is repeatedly divided by points chosen successively at random, the nth point being uniformly distributed over the largest of the n intervals formed by the first n − 1 points. Is this sequence of points asymptotically uniformly distributed?

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43

Denisov, S. I. "Fractal Dimension of Random Processes." Chaos, Solitons & Fractals 9, no. 9 (September 1998): 1491–96. http://dx.doi.org/10.1016/s0960-0779(97)00179-3.

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44

Telksnys, L. "Recognition of Nonstationary Random Processes." IFAC Proceedings Volumes 19, no. 5 (May 1986): 31–36. http://dx.doi.org/10.1016/s1474-6670(17)59763-3.

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45

Samaras, Elias, Masanobu Shinzuka, and Akira Tsurui. "ARMA Representation of Random Processes." Journal of Engineering Mechanics 111, no. 3 (March 1985): 449–61. http://dx.doi.org/10.1061/(asce)0733-9399(1985)111:3(449).

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46

Rothmann, Mark D., and Hammou El Barmi. "Stochastic processes involving random deletion." Journal of Applied Probability 38, no. 01 (March 2001): 95–107. http://dx.doi.org/10.1017/s0021900200018532.

Testo completo

Abstract (sommario):

We consider a system where units having magnitudes arrive according to a nonhomogeneous Poisson process, remain there for a random period and then depart. Eventually, at any point in time only a portion of those units which have entered the system remain. Of interest are the finite time properties and limiting behaviors of the distribution of magnitudes among the units present in the system and among those which have departed from the system. We will derive limiting results for the empirical distribution of magnitudes among the active (departed) units. These results are also shown to extend to systems having stages or steps through which units must proceed. Examples are given to illustrate these results.

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47

Stadje, W., and S. Zacks. "Telegraph processes with random velocities." Journal of Applied Probability 41, no. 03 (September 2004): 665–78. http://dx.doi.org/10.1017/s0021900200020465.

Testo completo

Abstract (sommario):

We study a one-dimensional telegraph process (Mt)t≥0describing the position of a particle moving at constant speed between Poisson times at which new velocities are chosen randomly. The exact distribution ofMtand its first two moments are derived. We characterize the level hitting times ofMtin terms of integro-differential equations which can be solved in special cases.

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48

Vodák, Rostislav, Michal Bíl, and Jiří Sedoník. "Network robustness and random processes." Physica A: Statistical Mechanics and its Applications 428 (June 2015): 368–82. http://dx.doi.org/10.1016/j.physa.2015.01.056.

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49

Bednorz, Witold. "Hölder Continuity of Random Processes." Journal of Theoretical Probability 20, no. 4 (May 9, 2007): 917–34. http://dx.doi.org/10.1007/s10959-007-0094-x.

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50

Shen, Qiang, Ruiqing Zhao, and Wansheng Tang. "Random fuzzy alternating renewal processes." Soft Computing 13, no. 2 (April 22, 2008): 139–47. http://dx.doi.org/10.1007/s00500-008-0307-y.

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