Relevant bibliographies by topics / Random processes

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Random processes'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Random processes"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Random processes"

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Makai, Tamas. "Random graph processes." Thesis, Royal Holloway, University of London, 2012. http://repository.royalholloway.ac.uk/items/b24b89af-3fc1-4d2f-a673-64483a3bc2f2/8/.

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This thesis deals with random graph processes. More precisely it deals with two random graph processes which create H -free graphs. The first of these processes is the random H-elimination process which starts from the complete graph and in every step removes an edge uniformly at random from the set of edges which are found in a copy of H. The second is the H-free random graph process which starts from the empty graph and in every step an edge chosen uniformly at random from the set of edges which when added to the graph would not create a copy of H is inserted. We consider these graph processes for several classes of graphs H, for example strictly two balanced graphs. The class of strictly two balanced graphs includes among others cycles and complete graphs. We analysed the H-elimination process, when H is strictly 2-balanced. For this class we show the typical number of edges found at the end of the process. We also consider the sub graphs created by the process and its independence number. We also managed to show the expected number of edges in the H -elimination pro- cess when H = Ki, the graph created from the complete graph on 4 vertices by removing an edge and when H = K34 where K34 is created from the complete bi- partite graph with 3 vertices in one partition and'4 vertices in the second partition, by removing an edge. In case of the H -free process we considered the case when H is the triangle and showed that the triangle-free random graph process only creates sparse subgraphs. Finally we have improved the lower bound on the length of the K34-free random graph process. '
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Kang, Mihyun. "Random planar structures and random graph processes." Doctoral thesis, [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=985516585.

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Zielen, Frank H. "Asymmetric random average processes." [S.l.] : [s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=965270475.

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Timar, Adam. "Group-invariant random processes." [Bloomington, Ind.] : Indiana University, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3204537.

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Thesis (Ph.D.)--Indiana University, Dept. of Mathematics, 2006.<br>Source: Dissertation Abstracts International, Volume: 67-01, Section: B, page: 0308. Adviser: Russell Lyons. "Title from dissertation home page (viewed Feb. 9, 2007)."
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Ortgiese, Marcel. "Stochastic processes in random environment." Thesis, University of Bath, 2009. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507234.

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We are interested in two probabilistic models of a process interacting with a random environment. Firstly, we consider the model of directed polymers in random environment. In this case, a polymer, represented as the path of a simple random walk on a lattice, interacts with an environment given by a collection of time-dependent random variables associated to the vertices. Under certain conditions, the system undergoes a phase transition from an entropy-dominated regime at high temperatures, to a localised regime at low temperatures. Our main result shows that at high temperatures, even though a central limit theorem holds, we can identify a set of paths constituting a vanishing fraction of all paths that supports the free energy. We compare the situation to a mean-field model defined on a regular tree, where we can also describe the situation at the critical temperature. Secondly, we consider the parabolic Anderson model, which is the Cauchy problem for the heat equation with a random potential. Our setting is continuous in time and discrete in space, and we focus on time-constant, independent and identically distributed potentials with polynomial tails at infinity. We are concerned with the long-term temporal dynamics of this system. Our main result is that the periods, in which the profile of the solutions remains nearly constant, are increasing linearly over time, a phenomenon known as ageing. We describe this phenomenon in the weak sense, by looking at the asymptotic probability of a change in a given time window, and in the strong sense, by identifying the almost sure upper envelope for the process of the time remaining until the next change of profile. We also prove functional scaling limit theorems for profile and growth rate of the solution of the parabolic Anderson model.
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Warnke, Lutz. "Random graph processes with dependencies." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:71b48e5f-a192-4684-a864-ea9059a25d74.

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Random graph processes are basic mathematical models for large-scale networks evolving over time. Their systematic study was pioneered by Erdös and Rényi around 1960, and one key feature of many 'classical' models is that the edges appear independently. While this makes them amenable to a rigorous analysis, it is desirable, both mathematically and in terms of applications, to understand more complicated situations. In this thesis the main goal is to improve our rigorous understanding of evolving random graphs with significant dependencies. The first model we consider is known as an Achlioptas process: in each step two random edges are chosen, and using a given rule only one of them is selected and added to the evolving graph. Since 2000 a large class of 'complex' rules has eluded a rigorous analysis, and it was widely believed that these could give rise to a striking and unusual phenomenon. Making this explicit, Achlioptas, D'Souza and Spencer conjectured in Science that one such rule yields a very abrupt (discontinuous) percolation phase transition. We disprove this, showing that the transition is in fact continuous for all Achlioptas process. In addition, we give the first rigorous analysis of the more 'complex' rules, proving that certain key statistics are tightly concentrated (i) in the subcritical evolution, and (ii) also later on if an associated system of differential equations has a unique solution. The second model we study is the H-free process, where random edges are added subject to the constraint that they do not complete a copy of some fixed graph H. The most important open question for such 'constrained' processes is due to Erdös, Suen and Winkler: in 1995 they asked what the typical final number of edges is. While Osthus and Taraz answered this in 2000 up to logarithmic factors for a large class of graphs H, more precise bounds are only known for a few special graphs. We close this gap for the cases where a cycle of fixed length is forbidden, determining the final number of edges up to constants. Our result not only establishes several conjectures, it is also the first which answers the more than 15-year old question of Erdös et. al. for a class of forbidden graphs H.
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Ding, Xinhong Carleton University Dissertation Mathematics. "Diffusion processes with random interactions." Ottawa, 1992.

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Seierstad, Taral Guldahl. "The phase transition in random graphs and random graph processes." Doctoral thesis, [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=985760044.

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Hu, Yilei. "Essays on random processes with reinforcement." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.669991.

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Figwer, Jarosław. "Synthesis and simulation of random processes." Praca habilitacyjna, Wydawnictwo Politechniki Śląskiej, 1999. https://delibra.bg.polsl.pl/dlibra/docmetadata?showContent=true&id=8070.

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Books on the topic "Random processes"

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Grimmett, Geoffrey. Probability and random processes. 3rd ed. Oxford: Oxford University Press, 2004.

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Skorokhod, A. V. Random processes with independent increments. Dordrecht: Kluwer Academic Publishers, 1991.

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David, Stirzaker, ed. Probability and random processes. 3rd ed. Oxford: Oxford University Press, 2001.

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David, Stirzaker, ed. Probability and random processes. 2nd ed. Oxford: Clarendon Press, 1992.

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David, Stirzaker, ed. Probability and random processes. Oxford [England]: Clarendon Press, 1992.

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Harnad, John, ed. Random Matrices, Random Processes and Integrable Systems. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9514-8.

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Grami, Ali. Probability, Random Variables, Statistics, and Random Processes. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119300847.

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Hsu, Hwei P. Schaum's outlines: Probability, random variables & random processes. 2nd ed. New York: McGraw-Hill, 2011.

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Leszek, Gawarecki, ed. Stochastic analysis for Gaussian random processes and fields: With applications. Boca Raton: CRC Press, Taylor & Francis Group, 2016.

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Krishnan, Venkatarama. Probability and Random Processes. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471998303.

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Book chapters on the topic "Random processes"

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Wang, Yingguang. "Random Processes." In Random Vibrations, 68–82. Boca Raton: CRC Press, 2024. https://doi.org/10.1201/9781003431503-3.

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Harwit, Martin. "Random Processes." In Astrophysical Concepts, 104–58. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4757-2019-8_4.

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Carlton, Matthew A., and Jay L. Devore. "Random Processes." In Probability with Applications in Engineering, Science, and Technology, 489–562. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52401-6_7.

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Gebali, Fayez. "Random Processes." In Analysis of Computer and Communication Networks, 1–16. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74437-7_2.

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Harwit, Martin. "Random Processes." In Astrophysical Concepts, 97–148. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2928-3_4.

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Carlton, Matthew A., and Jay L. Devore. "Random Processes." In Probability with Applications in Engineering, Science, and Technology, 597–682. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0395-5_7.

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Chonavel, Thierry. "Random Processes." In Statistical Signal Processing, 9–21. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0139-0_2.

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Preumont, André. "Random Processes." In Random Vibration and Spectral Analysis, 35–56. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-017-2840-9_3.

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Rozanov, Yu A. "Random Processes." In Probability Theory, Random Processes and Mathematical Statistics, 91–129. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0449-4_2.

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Cipra, Tomas. "Random Processes." In Time Series in Economics and Finance, 5–38. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46347-2_2.

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Conference papers on the topic "Random processes"

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Vélez, Ricardo, and Tomás Prieto-Rumeau. "Random Assignment Processes." In Annual International Conference on Operations Research and Statistics. Global Science & Technology Forum (GSTF), 2012. http://dx.doi.org/10.5176/2251-1938_ors46.

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Wilkowski, D., R. Kaiser, G. Labeyrie, C. A. Müller, Ch Miniatura, T. Wellens, B. Grémaud, and D. Delande. "Light transport in cold atoms: dephasing processes." In Photonic Metamaterials: From Random to Periodic. Washington, D.C.: OSA, 2007. http://dx.doi.org/10.1364/meta.2007.thc4.

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Cohen, Leon. "Classical and Quantum Random Processes." In INFORMATION OPTICS: 5th International Workshop on Information Optics (WIO'06). AIP, 2006. http://dx.doi.org/10.1063/1.2361208.

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Treviño, George, Jay Hardin, Bruce Douglas, and Edgar Andreas. "Current Topics in Nonstationary Analysis." In Second Workshop on Nonstationary Random Processes and Their Applications. WORLD SCIENTIFIC, 1996. http://dx.doi.org/10.1142/9789812833099.

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Si, Si. "Gaussian processes and Gaussian random fields." In Proceedings of the Second International Conference. WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812792761_0015.

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Chernoyarov, Oleg. "Digital simulators of the random processes." In The 33rd European Modeling & Simulation Symposium. CAL-TEK srl, 2021. http://dx.doi.org/10.46354/i3m.2021.emss.007.

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Thomson, D. J. "Reconstruction of unequally-sampled random processes." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628696.

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Galleani, Lorenzo, Leon Cohen, and Bruce Suter. "Locally Stationary Noise and Random Processes." In INFORMATION OPTICS: 5th International Workshop on Information Optics (WIO'06). AIP, 2006. http://dx.doi.org/10.1063/1.2361257.

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Azzi, S., Y. Huang, B. Sudret, and J. Wiart. "Random Processes Metamodeling Applied to Dosimetry." In 2018 2nd URSI Atlantic Radio Science Meeting (AT-RASC). IEEE, 2018. http://dx.doi.org/10.23919/ursi-at-rasc.2018.8471520.

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Li, Yanbin, Sameer B. Mulani, Rakesh K. Kapania, Shaoqing Wu, and Qingguo Fei. "Non-Stationary Random Vibration Analysis Using Multi-Correlated Random Processes Excitations." In 57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-2173.

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Reports on the topic "Random processes"

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Freidlin, Mark. PDE's, Random Processes and Fields: Asymptotic Problems. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada304572.

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Leadbetter, M. R. On the Exeedance Random Measures for Stationary Processes. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada192838.

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Voychishin, K. S., and Ya P. Dragan. Elimination Of Rhythm For Periodically Correlated Random Processes. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada261061.

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Young, Richard M. Modeling Random Walk Processes In Human Concept Learning. Fort Belvoir, VA: Defense Technical Information Center, May 2006. http://dx.doi.org/10.21236/ada462700.

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Michels, James H. Synthesis of Multichannel Autoregressive Random Processes and Ergodicity Considerations. Fort Belvoir, VA: Defense Technical Information Center, July 1990. http://dx.doi.org/10.21236/ada226493.

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Slavtchova-Bojkova, Maroussia N., Ollivier Hyrien, and Nikolay M. Yanev. Poisson Random Measures and Noncritical Multitype Markov Branching Processes. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, May 2021. http://dx.doi.org/10.7546/crabs.2021.05.03.

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Lunsford, Kurt G., and Kenneth D. West. Random Walk Forecasts of Stationary Processes Have Low Bias. Federal Reserve Bank of Cleveland, August 2023. http://dx.doi.org/10.26509/frbc-wp-202318.

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We study the use of a zero mean first difference model to forecast the level of a scalar time series that is stationary in levels. Let bias be the average value of a series of forecast errors. Then the bias of forecasts from a misspecified ARMA model for the first difference of the series will tend to be smaller in magnitude than the bias of forecasts from a correctly specified model for the level of the series. Formally, let P be the number of forecasts. Then the bias from the first difference model has expectation zero and a variance that is O(1/P²), while the variance of the bias from the levels model is generally O(1/P). With a driftless random walk as our first difference model, we confirm this theoretical result with simulations and empirical work: random walk bias is generally one-tenth to one-half that of an appropriately specified model fit to levels.
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Alfano, R. R., F. Liu, Y. Guo, C. H. Liu, and J. Ying. Optical Amplification and Nonlinear Optical Processes in Random Scattering Media. Fort Belvoir, VA: Defense Technical Information Center, April 2000. http://dx.doi.org/10.21236/ada377025.

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Getoor, R. K., and Joseph Glover. Constructing Markov Processes with Random Times of Birth and Death,. Fort Belvoir, VA: Defense Technical Information Center, January 1986. http://dx.doi.org/10.21236/ada171856.

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Michels, James H. Correlation Function Estimator Performance in Non-Gaussian Spherically Invariant Random Processes. Fort Belvoir, VA: Defense Technical Information Center, October 1993. http://dx.doi.org/10.21236/ada273498.

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