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Books on the topic 'Model of random process'

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Published: 30 May 2022
Last updated: 31 May 2022

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1

Schreiber, Sebastian J. Urn models, replicator process and random genetic drift. [Philadelphia, Pa.]: Society for Industrial and Applied Mathematics, 2001.

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2

Bleher, Pavel. Random matrices and the six-vertex model. Providence, Rhode Island, USA: American Mathematical Society, 2014.

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3

Srivastava, M. S. Economical on-line quality control procedures based on normal random walk model with measurement error. Toronto, Ont: University of Toronto, Dept. of Statistics, 1993.

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4

Srivastava, M. S. Economical quality control procedures based on integrated moving average process of order one. Toronto: University of Toronto, Dept. of Statistics, 1993.

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5

Gaver, Donald Paul. Random parameter Markov population process models and their likelihood, Bayes, and empirical Bayes analysis. Monterey, Calif: Naval Postgraduate School, 1985.

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6

Duflo, Marie. Random iterative models. Berlin: Springer, 1997.

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7

Random field models in earth sciences. Mineola, N.Y: Dover Publications, 2005.

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8

Random field models in earth sciences. San Diego: Academic Press, 1992.

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9

Grimmett, Geoffrey. The Random-Cluster Model. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-32891-9.

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10

I͡U︡, Kuznet͡s︡ov N., and Shurenkov V. M, eds. Models of random processes: Handbook for Mathematicians and Engineers. Boca Raton, Florida: CRC Press, 1996.

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11

S, Taqqu Murad, ed. Stable non-Gaussian random processes: Stochastic models with infinite variance. New York: Chapman & Hall, 1994.

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12

R. W. van der Hofstad. One-dimensional random polymers. Amsterdam, The Netherlands: CWI, 1998.

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13

Matěj, Bílý, and Bukoveczky Juraj, eds. Random processes: Measurement, analysis, and simulation. Amsterdam: Elsevier, 1988.

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14

Jean, Bertoin, Martinelli F, Peres Y, Bernard P. 1944-, Bertoin Jean, Martinelli F, and Peres Y, eds. Lectures on probability theory and statistics: Ecole d'été de probabilités de Saint-Flour XXVII, 1997. Berlin: Springer, 2000.

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15

Ecole d'été de probabilités de Saint-Flour (27th 1997). Lectures on probability theory and statistics: Ecole d'eté de probabilités de Saint-Flour XXVII, 1997. Edited by Bertoin Jean, Martinelli F, Peres Y, and Bernard P. 1944-. Berlin: Springer, 1999.

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16

Kucza, Timo. Knowledge management process model. Espoo [Finland]: Technical Research Centre of Finland, 2001.

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17

Larsen, Curtis E. Random process simulation for stochastic fatigue analysis. [Washington, D.C.]: National Aeronautics and Space Administration, 1988.

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18

Larsen, Curtis E. Random process simulation for stochastic fatigue analysis. [Washington, D.C.]: National Aeronautics and Space Administration, 1988.

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19

Schlicht, Ekkehart. Variance estimation in a random coefficients model. Bonn, Germany: IZA, 2006.

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20

Vanhonacker, Wilfried R. "A rational random behavior model of choice". Fontainbleau: INSEAD, 1986.

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21

Dunson, David B., ed. Random Effect and Latent Variable Model Selection. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-76721-5.

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22

Hunt, J. G. Pedestrian crossing criteria research: Random crossing model. Crowthorne: Transport and Road Research Laboratory, 1991.

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23

Haghighi, Aliakbar Montazer. Queuing models in industry and business. New York: Nova Science Publishers, 2007.

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24

Svishchuk, A. V. Random evolutions and their applications. Dordrecht: Kluwer, 1997.

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25

Berber, Ridvan. Nonlinear Model Based Process Control. Dordrecht: Springer Netherlands, 1998.

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26

Ansari, Rashid M., and Moses O. Tadé. Nonlinear Model-based Process Control. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0739-2.

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27

Mendling, Jan, and Matthias Weidlich, eds. Business Process Model and Notation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33155-8.

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28

Berber, Ridvan, and Costas Kravaris, eds. Nonlinear Model Based Process Control. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5094-1.

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29

Dijkman, Remco, Jörg Hofstetter, and Jana Koehler, eds. Business Process Model and Notation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25160-3.

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30

Rozhenko, A. I. Teorii︠a︡ i algoritmy variat︠s︡ionnoĭ splaĭn-approksimat︠s︡ii. Novosibirsk: Institut vychislitelʹnoĭ matematiki i matematicheskoĭ geofiziki, 2005.

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31

Prigarin, S. M. Metody chislennogo modelirovanii︠a︡ sluchaĭnykh prot︠s︡essov i poleĭ. Novosibirsk: IVMiMG SO RAN, 2005.

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32

M. C. M. de Gunst. A random model for plant cell population growth. [Amsterdam, the Netherlands]: Centrum voor Wiskunde en Informatica, 1989.

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33

A random tiling model for two-dimensional electrostatics. Providence, RI: American Mathematical Society, 2005.

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34

Iosifescu, Marius. Stochastic Processes and Applications in Biology and Medicine II: Models: 4. 2nd ed. Bucareşti: Editura Academiei, 2011.

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35

Slater, Roger. Integrated process management: A quality model. New York: McGraw-Hill, 1991.

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36

Russell, Jeffrey S. A model process for maintainability implementation. Austin, TX: [Construction Industry Institute, the University of Texas at Austin, 1999.

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37

Berber, Ridvan, ed. Methods of Model Based Process Control. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0135-6.

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38

Stampfl, Georg. The Process of Business Model Innovation. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-11266-0.

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39

Berber, Ridvan. Methods of Model Based Process Control. Dordrecht: Springer Netherlands, 1995.

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40

Cook, Curtis R. New and improved documentation process model. Corvallis, OR: Oregon State University, Dept. of Computer Science, 1996.

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41

Svishchuk, A. V. Random evolutions and their applications: New trends. Dordrecht, The Netherlands: Kluwer Academic Publishers, 2000.

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42

The Random-Cluster Model (Grundlehren der mathematischen Wissenschaften). Springer, 2006.

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43

Nosofsky, Robert M., and Thomas J. Palmeri. An Exemplar-Based Random-Walk Model of Categorization and Recognition. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.7.

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Abstract:
In this chapter, we provide a review of a process-oriented mathematical model of categorization known as the exemplar-based random-walk (EBRW) model (Nosofsky & Palmeri, 1997a). The EBRW model is a member of the class of exemplar models. According to such models, people represent categories by storing individual exemplars of the categories in memory, and classify objects on the basis of their similarity to the stored exemplars. The EBRW model combines ideas ranging from the fields of choice and similarity, to the development of automaticity, to response-time models of evidence accumulation and decision-making. This integrated model explains relations between categorization and other fundamental cognitive processes, including individual-object identification, the development of expertise in tasks of skilled performance, and old-new recognition memory. Furthermore, it provides an account of how categorization and recognition decision-making unfold through time. We also provide comparisons with some other process models of categorization.
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44

Majumdar, Satya N. Random growth models. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.38.

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This article discusses the connection between a particular class of growth processes and random matrices. It first provides an overview of growth model, focusing on the TASEP (totally asymmetric simple exclusion process) with parallel updating, before explaining how random matrices appear. It then describes multi-matrix models and line ensembles, noting that for curved initial data the spatial statistics for large time t is identical to the family of largest eigenvalues in a Gaussian Unitary Ensemble (GUE multi-matrix model. It also considers the link between the line ensemble and Brownian motion, and whether this persists on Gaussian Orthogonal Ensemble (GOE) matrices by comparing the line ensembles at fixed position for the flat polynuclear growth model (PNG) and at fixed time for GOE Brownian motions. Finally, it examines (directed) last passage percolation and random tiling in relation to growth models.
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45

Grimmett, Geoffrey R. The Random-Cluster Model (Grundlehren der mathematischen Wissenschaften Book 333). Springer Berlin Heidelberg, 2006.

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46

Zimmerman, Dale L. Linear Model Theory: Exercises and Solutions. Springer, 2020.

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47

Zimmerman, Dale L. Linear Model Theory: With Examples and Exercises. Springer, 2020.

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48

Its, Alexander R. Random matrix theory and integrable systems. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.10.

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This article discusses the interaction between random matrix theory (RMT) and integrable theory, leading to ordinary and partial differential equations (PDEs) for the eigenvalue distribution of random matrix models of size n and the transition probabilities of non-intersecting Brownian motion models, for finite n and for n → ∞. It first provides an overview of the connection between the theory of orthogonal polynomials and the KP-hierarchy in integrable systems before examining matrix models and the Virasoro constraints. It then considers multiple orthogonal polynomials, taking into account non-intersecting Brownian motions on ℝ (Dyson’s Brownian motions), a moment matrix for several weights, Virasoro constraints, and a PDE for non-intersecting Brownian motions. It also analyses critical diffusions, with particular emphasis on the Airy process, the Pearcey process, and Airy process with wanderers. Finally, it describes the Tacnode process, along with kernels and p-reduced KP-hierarchy.
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49

Butyrskiy, Evgeniy. Methods for modeling and estimating random variables and processes. Strategy of the Future, 2020. http://dx.doi.org/10.37468/mon_1850.

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The monograph introduces the basics of theory and practice of mathematical research methods of stochastic systems and processes. It examines the models, methods of describing and forming random events, values and processes, as well as methods of their optimal and suboptimal assessment. The monograph can be useful for a wide range of specialists in various fields of expertise in mathematical and statistical modeling in their research, and can also be used in the learning process to conduct both classroom, and independent theoretical and practical classes with students and masters of St. Petersburg State University engaged in the program «Mathematical modeling» and «Optimal and suboptimal assessment of random processes and systems».
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50

Tulino, Antonia, and Sergio Verdu. Random matrix theory and ribonucleic acid (RNA) folding. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.42.

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This article discusses a series of recent applications of random matrix theory (RMT) to the problem of RNA folding. It first provides a schematic overview of the RNA folding problem, focusing on the concept of RNA pseudoknots, before considering a simplified framework for describing the folding of an RNA molecule; this framework is given by the statistic mechanical model of a polymer chain of L nucleotides in three dimensions with interacting monomers. The article proceeds by presenting a physical interpretation of the RNA matrix model and analysing the large-N expansion of the matrix integral, along with the pseudoknotted homopolymer chain. It extends previous results about the asymptotic distribution of pseudoknots of a phantom homopolymer chain in the limit of large chain length.
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