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Books on the topic 'Stochastic processes with large dimension'

Author: Grafiati
Published: 12 October 2024

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1

1939-, Tzafestas S. G., and Watanabe Keigo 1952-, eds. Stochastic large-scale engineering systems. New York: M. Dekker, 1992.

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2

Bosq, Denis. Inference and prediction in large dimensions. Hoboken, NJ: John Wiley, 2007.

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3

Girko, V. L. Statistical analysis of observations of increasing dimension. Dordrecht: Kluwer Academic Publishers, 1995.

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4

G, Kurtz Thomas, ed. Large deviations for stochastic processes. Providence, R.I: American Mathematical Society, 2006.

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5

Assing, Sigurd. Continuous strong Markov processes in dimension one: A stochastic calculus approach. Berlin: Springer, 1998.

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6

Wentzell, A. D. Limit Theorems on Large Deviations for Markov Stochastic Processes. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-1852-8.

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7

Wentzell, Alexander D. Limit theorems on large deviations for Markov stochastic processes. Dordrecht: Kluwer Academic Publishers, 1990.

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8

Wentzell, A. D. Limit Theorems on Large Deviations for Markov Stochastic Processes. Dordrecht: Springer Netherlands, 1990.

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9

Vaart, A. W. van der. Statistical estimation in large parameter spaces. Amsterdam: CWI, 1987.

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10

A. W. van der Vaart. Statistical estimation in large parameter spaces. Amsterdam, Netherlands: Centre for Mathematics and Computer Science, 1988.

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11

Ecole d'été de probabilités de Saint-Flour. (15th 1985). École d'été de probabilités de Saint Flour XV-XVII-1985-87. Edited by Hennequin Paul Louis, Ecole d'été de probabilités de Saint-Flour. (16th : 1986), and Ecole d'été de probabilités de Saint-Flour. (17th : 1987). Berlin: Springer-Verlag, 1988.

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12

Feng, Zhaoshu. Stability analysis and stabilization synthesis of stochastic large scale systems. Beijing: Science Press, 1995.

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13

Ecole d'été de probabilités de Saint-Flour (15th-17th 1985-1987). Ecole d'été de probabilités de Saint-Flour XV-XVII, 1985-87. Edited by Diaconis Persi and Hennequin Paul Louis. Berlin: Springer-Verlag, 1988.

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14

NATO, Advanced Study Institute on Large-Scale Molecular Systems: Quantum and Stochastic Aspects-Beyond the Simple Molecular Picture (1990 Acquafredda di Maratea Italy). Large-scale molecular systems: Quantum and stochastic aspects--beyond the simple molecular picture. New York: Plenum Press, 1991.

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15

O, Seppäläinen Timo, ed. A course on large deviations with an introduction to Gibbs measures. Providence, Rhode Island: American Mathematical Society, 2015.

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16

Dembo, Amir. Large deviations techniques and applications. Boston: Jones and Bartlett, 1993.

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17

Dembo, Amir. Large deviations techniques and applications. 2nd ed. New York: Springer, 1998.

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18

Herrmann, Samuel. Stochastic resonance: A mathematical approach in the small noise limit. Providence, Rhode Island: American Mathematical Society, 2014.

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19

Mandelbrot, Benoit B. Les objets fractals: Forme, hasard et dimension. 3rd ed. [Paris]: Flammarion, 1989.

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20

1951-, Durrett Richard, American Mathematical Society, Institute of Mathematical Statistics, and Society for Industrial and Applied Mathematics., eds. Particle systems, random media, and large deviations. Providence, R.I: American Mathematical Society, 1985.

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21

He, Qi. System Identification Using Regular and Quantized Observations: Applications of Large Deviations Principles. New York, NY: Springer New York, 2013.

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22

Fiore, Alessio, and Luigi Provero, eds. La signoria rurale nell’Italia del tardo medioevo. 3 L’azione politica locale. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-427-4.

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Abstract:
Rural lordship is one of the classic medieval themes of recent decades, but its specific developments of the late Middle Ages have long been neglected by research, especially engaged in considering other processes in these centuries, such as the construction of regional states, the economic dynamics, the urban riots. This volume, as part of a large research project coordinated by Sandro Carocci, intends to help fill this gap by offering a wide sample of cases, relating to very different areas of the Italian peninsula. Local realities are investigated here from a very specific perspective, that is, in their strictly political dimension: while taking into account the broad contexts (economic, settlement and social) in which the rural lordships are located, the main questions of this volume focus on forms of the lordly domain and its relations with the subjects, with the regional states and with the other lordly powers.
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23

Schehr, Grégory, Alexander Altland, Yan V. Fyodorov, Neil O'Connell, and Leticia F. Cugliandolo, eds. Stochastic Processes and Random Matrices. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.001.0001.

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The field of stochastic processes and random matrix theory (RMT) has been a rapidly evolving subject during the past fifteen years where the continuous development and discovery of new tools, connections, and ideas have led to an avalanche of new results. These breakthroughs have been made possible thanks, to a large extent, to the recent development of various new techniques in RMT. Matrix models have been playing an important role in theoretical physics for a long time and they are currently also a very active domain of research in mathematics. An emblematic example of these recent advances concerns the theory of growth phenomena in the Kardar–Parisi–Zhang (KPZ) universality class where the joint efforts of physicists and mathematicians during the past twenty years have unveiled the beautiful connections between this fundamental problem of statistical mechanics and the theory of random matrices, namely the fluctuations of the largest eigenvalue of certain ensemble of random matrices. These chapters not only cover this topic in detail but also present more recent developments that have emerged from these discoveries, for instance in the context of low-dimensional heat transport (on the physics side) or in the context of integrable probability (on the mathematical side).
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24

Girko, V. L. Statistical Analysis of Observations of Increasing Dimension. Springer, 2013.

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25

Girko, V. L. Statistical Analysis of Observations of Increasing Dimension. Springer, 2010.

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26

Kurtz, Thomas G., and Jin Feng. Large Deviations for Stochastic Processes. American Mathematical Society, 2006.

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27

Higle, Julia L., and S. Sen. Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming. Springer, 2013.

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28

Higle, Julia L., and S. Sen. Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming. Springer London, Limited, 2013.

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29

Assing, Sigurd, and Wolfgang M. Schmidt. Continuous Strong Markov Processes in Dimension One: A Stochastic Calculus Approach. Springer London, Limited, 2006.

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30

Fabbri, Giorgio, Andrzej Święch, Marco Fuhrman, Gianmario Tessitore, and Fausto Gozzi. Stochastic Optimal Control in Infinite Dimension: Dynamic Programming and HJB Equations. Springer, 2018.

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31

Fabbri, Giorgio, Andrzej Święch, Marco Fuhrman, Gianmario Tessitore, and Fausto Gozzi. Stochastic Optimal Control in Infinite Dimension: Dynamic Programming and HJB Equations. Springer, 2017.

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32

Hami, Abdelkhalak El, and Bouchaib Radi. Dynamics of Large Structures and Inverse Problems. Wiley & Sons, Incorporated, John, 2017.

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33

Hami, Abdelkhalak El, and Bouchaib Radi. Dynamics of Large Structures and Inverse Problems. Wiley & Sons, Incorporated, John, 2017.

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34

Hami, Abdelkhalak El, and Bouchaib Radi. Dynamics of Large Structures and Inverse Problems. Wiley & Sons, Incorporated, John, 2017.

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35

Hami, Abdelkhalak El, and Bouchaib Radi. Dynamics of Large Structures and Inverse Problems. Wiley & Sons, Incorporated, John, 2017.

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36

Stochastic Optimization for Large-Scale Machine Learning. Taylor & Francis Group, 2021.

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37

Chauhan, Vinod Kumar. Stochastic Optimization for Large-Scale Machine Learning. Taylor & Francis Group, 2021.

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38

Chauhan, Vinod Kumar. Stochastic Optimization for Large-Scale Machine Learning. CRC Press LLC, 2021.

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39

Chauhan, Vinod Kumar. Stochastic Optimization for Large-Scale Machine Learning. Taylor & Francis Group, 2021.

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40

Nonparametric studies of doubly stochastic Poisson processes, binomial data, and high dimension, low sample size data. 2008.

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41

Large Deviations Techniques And Applications. Springer, 2009.

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42

Sen, Pranab K., and Julio M. Singer. Large Sample Methods in Statistics: An Introduction with Applications. Taylor & Francis Group, 2017.

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43

Sen, Pranab K., and Julio M. Singer. Large Sample Methods in Statistics: An Introduction with Applications. Chapman & Hall/CRC, 1994.

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44

Sen, Pranab K., and Julio M. Singer. Large Sample Methods in Statistics: An Introduction with Applications. Taylor & Francis Group, 2017.

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45

Sen, Pranab K., and Julio M. Singer. Large Sample Methods in Statistics: An Introduction with Applications. Taylor & Francis Group, 2017.

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46

Sen, Pranab K., and Julio M. Singer. Large Sample Methods in Statistics: An Introduction with Applications. Taylor & Francis Group, 2017.

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47

Sen, Pranab K., and Julio M. Singer. Large Sample Methods in Statistics: An Introduction with Applications. Taylor & Francis Group, 2017.

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48

Englander, Janos. Spatial Branching in Random Environments and with Interaction. World Scientific Publishing Co Pte Ltd, 2014.

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49

Zhaoshu, Feng, and Liu Yongqing. Stability Analysis and Stabilization Synthesis of Stoehastic Large Scale Systems. Science Pr, 1999.

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50

Mandelbrot, Benoit B. Fractals: Form, Chance, and Dimension. Echo Point Books and Media, 2020.

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