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Artículos de revistas sobre el tema "Random processes"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 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 t

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2

Alexander, Kenneth S., and Steven A. Kalikow. "Random Stationary Processes." Annals of Probability 20, no. 3 (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 (2000): 33–43. http://dx.doi.org/10.1017/s096354839900406x.

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4

Bandt, Christoph. "Ordinal Random Processes." Entropy 27, no. 6 (2025): 610. https://doi.org/10.3390/e27060610.

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Ordinal patterns have proven to be a valuable tool in many fields. Here, we address the need for theoretical models. A paradigmatic example shows that a model for frequencies of ordinal patterns can be determined without any numerical values. We specify the important concept of stationary order and the fundamental problems to be solved in order to establish a genuine statistical methodology for ordinal time series.

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5

Aitken, G. J. M. "Illustrating Random Processes with Random Phase Modulation." International Journal of Electrical Engineering & Education 23, no. 2 (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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6

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

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7

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

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8

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

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9

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

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10

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

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11

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

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12

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

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13

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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14

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

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15

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

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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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16

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

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17

Clifford, Peter, and David Stirzaker. "History-dependent random processes." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464, no. 2093 (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, i

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18

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

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19

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

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20

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

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21

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

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22

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 proce

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23

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

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24

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

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25

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 (2001): 1526–27. http://dx.doi.org/10.1198/jasa.2001.s428.

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26

FRYZ, Mykhailo, and Bogdana MLYNKO. "DISCRETE-TIME CONDITIONAL LINEAR RANDOM PROCESSES AND THEIR PROPERTIES." Herald of Khmelnytskyi National University. Technical sciences 309, no. 3 (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 t

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27

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

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28

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

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29

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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30

LI, ZHIMING, QIN WANG, and YUANFANG WU. "WAVELET ANALYSIS FOR RANDOM PROCESSES." Modern Physics Letters A 16, no. 09 (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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31

Vanhoff, Barry, and Steve Elgar. "Simulating Quadratically Nonlinear Random Processes." International Journal of Bifurcation and Chaos 07, no. 06 (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 ser

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32

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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33

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

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34

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

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35

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

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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

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36

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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37

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 (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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38

Stadje, W., and S. Zacks. "Telegraph processes with random velocities." Journal of Applied Probability 41, no. 3 (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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39

Szász, Domokos. "Random Walks and Lorentz Processes." Entropy 26, no. 11 (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 an

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40

Howroyd, Douglas C., and Han Yu. "Assouad Dimension of Random Processes." Proceedings of the Edinburgh Mathematical Society 62, no. 1 (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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41

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

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42

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

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43

Kingman, J. F. C. "Random dissections and branching processes." Mathematical Proceedings of the Cambridge Philosophical Society 104, no. 1 (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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44

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

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45

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

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46

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

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47

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

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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

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48

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

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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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49

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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50

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

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