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Libros sobre el tema "Computational stochastic dynamics"

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Publicado: 6 de septiembre de 2023
Última modificación: 7 de septiembre de 2023

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1

Papadrakakis, Manolis, George Stefanou y Vissarion Papadopoulos, eds. Computational Methods in Stochastic Dynamics. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-9987-7.

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2

Winkelmann, Stefanie y Christof Schütte. Stochastic Dynamics in Computational Biology. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62387-6.

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3

Papadrakakis, Manolis, George Stefanou y Vissarion Papadopoulos, eds. Computational Methods in Stochastic Dynamics. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5134-7.

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4

Papadrakakis, Manolis. Computational Methods in Stochastic Dynamics: Volume 2. Dordrecht: Springer Netherlands, 2013.

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5

Öttinger, Hans Christian. Stochastic processes in polymeric fluids: Tools and examples for developing simulation algorithms. Berlin: Springer, 1996.

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6

Schinazi, Rinaldo B. Classical and Spatial Stochastic Processes. Boston, MA: Birkhäuser Boston, 1999.

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7

Nonlinear and Stochastic Beam Dynamics in Accelorators. (1993 Desy, Lüneburg). Nonlinear and stochastic beam dynamics in accelerators: A challenge to theoretical and computational physics, Lüneburg, September 29-October 3, 1997. Hamburg: Deutsches Elektronen-Synchrotron, 1998.

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8

Krzysztof, Szajowski y SpringerLink (Online service), eds. Advances in Dynamic Games: Theory, Applications, and Numerical Methods for Differential and Stochastic Games. Boston: Springer Science+Business Media, LLC, 2011.

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9

service), SpringerLink (Online, ed. Modeling Multi-Level Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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10

author, Sarich Marco 1985, ed. Metastability and Markov state models in molecular dynamics: Modeling, analysis, algorithmic approaches. Providence, Rhode Island: American Mathematical Society, 2013.

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11

Celledoni, Elena, Giulia Di Nunno, Kurusch Ebrahimi-Fard y Hans Zanna Munthe-Kaas, eds. Computation and Combinatorics in Dynamics, Stochastics and Control. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01593-0.

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12

Ransbotham, Sam. Sequential grid computing: Models and computational experiments. Bangalore: Indian Institute of Management Bangalore, 2009.

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13

Cardaliaguet, Pierre. Advances in Dynamic Games: Theory, Applications, and Numerical Methods for Differential and Stochastic Games. Boston: Birkhäuser Boston, 2012.

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14

Vamos¸, C˘alin. Automatic trend estimation. Dordrecht: Springer Netherlands, 2013.

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15

Manolis, Papadrakakis George Stefanou Vissarion Papadopoulos. Computational Methods in Stochastic Dynamics. Springer, 2011.

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16

Winkelmann, Stefanie y Christof Schütte. Stochastic Dynamics in Computational Biology. Springer International Publishing AG, 2021.

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17

Computational Methods In Stochastic Dynamics. Springer, 2010.

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18

Papadrakakis, Manolis, George Stefanou y Vissarion Papadopoulos. Computational Methods in Stochastic Dynamics. Springer, 2012.

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19

Papadrakakis, Manolis, George Stefanou y Vissarion Papadopoulos. Computational Methods in Stochastic Dynamics. Springer, 2011.

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20

Computational Methods in Stochastic Dynamics Computational Methods in Applied Sciences. Springer, 2012.

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21

Papadrakakis, Manolis, George Stefanou y Vissarion Papadopoulos. Computational Methods in Stochastic Dynamics: Volume 2. Springer, 2014.

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22

Papadrakakis, Manolis, George Stefanou y Vissarion Papadopoulos. Computational Methods in Stochastic Dynamics: Volume 2. Springer, 2012.

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23

Stochastic Dynamics for Systems Biology Chapman HallCRC Mathematical Computational Biology. Taylor & Francis Inc, 2014.

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24

Stochastic Processes in Polymeric Fluids: Tools and Examples for Developing Simulation Algorithms. Springer, 1996.

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25

Dynamics of Cancer: Mathematical Foundations of Oncology. World Scientific Publishing Co Pte Ltd, 2014.

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26

Schinazi, Rinaldo B. Classical and Spatial Stochastic Processes: With Applications to Biology. Birkhauser Verlag, 2014.

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27

Calligraphy lettering (Book Box). Readers Digest, 1998.

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28

Classical and Spatial Stochastic Processes: With Applications to Biology. Birkhäuser, 2014.

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29

Iordache, Octavian. Modeling Multi-Level Systems. Springer, 2014.

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30

Juillard, Michel. Dynamic Stochastic General Equilibrium Models. Editado por Shu-Heng Chen, Mak Kaboudan y Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.4.

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Dynamic Stochastic General Equilibrium (DSGE) models have become popular in macroeconomics, but the combination of nonlinear microeconomic behavior of the agents and model-consistent expectations raise intricate computational issues; this chapter reviews solution methods and estimation of DSGE models. Perfect foresight deterministic models can easily be solved with a great degree of accuracy. In practice, medium-sized stochastic models can only be solved by local approximation or the perturbation approach. The Bayesian approach to estimation is privileged. It provides a convenient way to communicate both the prior information available to the econo-metrician and new information revealed by the data. This chapter focuses on methods frequently used in applied work rather than aiming at being exhaustive.
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31

Koch, Christof. Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.001.0001.

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Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes. Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation. Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.
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32

Shreve, Steven E. y Dimitri P. Bertsekas. Stochastic Optimal Control: The Discrete-Time Case (Optimization and Neural Computation Series). Athena Scientific, 2007.

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33

Henderson, Daniel A., R. J. Boys, Carole J. Proctor y Darren J. Wilkinson. Linking systems biology models to data: A stochastic kinetic model of p53 oscillations. Editado por Anthony O'Hagan y Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.7.

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This article discusses the use of a stochastic kinetic model to study protein level oscillations in single living cancer cells, using the p53 and Mdm2 proteins as examples. It describes the refinement of a dynamic stochastic process model of the cellular response to DNA damage and compares this model to time course data on the levels of p53 and Mdm2. The article first provides a biological background on p53 and Mdm2 before explaining how the stochastic kinetic model is constructed. It then introduces the stochastic kinetic model and links it to the data and goes on to apply sophisticated MCMC methods to compute posterior distributions. The results demonstrate that it is possible to develop computationally intensive Markov chain Monte Carlo (MCMC) methods for conducting a Bayesian analysis of an intra-cellular stochastic systems biology model using single-cell time course data.
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34

(Editor), Adnan Ibrahimbegovic y Ivica Kozar (Editor), eds. Extreme Man-Made and Natural Hazards in Dynamics of Structures (NATO Security through Science Series / NATO Security through Science Series C: Environmental Security). Springer, 2007.

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35

Sekimoto, Ken. Stochastic Energetics. Springer, 2012.

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36

Stochastic Energetics. Springer, 2010.

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37

Sekimoto, Ken. Stochastic Energetics. Springer, 2010.

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38

Sekimoto, Ken. Stochastic Energetics. Springer, 2010.

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39

Cole, Harold L. Monetary and Fiscal Policy through a DSGE Lens. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190076030.001.0001.

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This text is designed to bridge the gap between Ph.D. and undergraduate textbooks in Macroeconomics. The text develops a dynamic stochastic general equilibrium model of money using a cash-in-advance constraint and endogenous production as in the real business cycle literature. The costs of inflation and optimal monetary policy, the impact of labor and capital taxes and as well as optimal fiscal policy are covered. Many extensions, including new Keynesian liquidity shock models are developed. Both standard analytic methods, such as Lagrangian methods, and computational methods using Matlab and Python, are developed as we construct quantitative models.
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40

Nunno, Giulia Di, Kurusch Ebrahimi-Fard, Elena Celledoni y Hans Zanna Munthe-Kaas. Computation and Combinatorics in Dynamics, Stochastics and Control: The Abel Symposium, Rosendal, Norway, August 2016. Springer, 2019.

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41

Numerical Methods for Controlled Stochastic Delay Systems. Springer London, Limited, 2008.

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42

Leondes, Cornelius T. Control and Dynamic Systems: Advances in Theory and Applications : Advances in Algorithms and Computational Techniques in Dynamic Systems Control, P (Control and Dynamic Systems). Academic Press, 1989.

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43

Advances in Dynamic Game Theory: Numerical Methods, Algorithms, and Applications to Ecology and Economics (Annals of the International Society of Dynamic Games Book 9). Birkhäuser, 2007.

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44

Advances in Dynamic Games and Applications. Springer, 2012.

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45

Haurie, Alain y Tamer Başar. Advances in Dynamic Games and Applications. Birkhauser Verlag, 2013.

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46

Tass, Peter A. Phase Resetting in Medicine and Biology: Stochastic Modelling and Data Analysis. Springer London, Limited, 2007.

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47

(Editor), Alain Haurie, Shigeo Muto (Editor), Leon A. Petrosjan (Editor) y T.E.S. Raghavan (Editor), eds. Advances in Dynamic Games: Applications to Economics, Management Science, Engineering, and Environmental Management (Annals of the International Society of Dynamic Games). Birkhäuser Boston, 2006.

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48

Phase Resetting in Medicine and Biology: Stochastic Modelling and Data Analysis (Springer Series in Synergetics). Springer-Verlag Telos, 1999.

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49

Advances in Dynamic Games: Applications to Economics, Management Science, Engineering, and Environmental Management. Springer London, Limited, 2007.

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50

Random Perturbation Methods with Applications in Science and Engineering. Springer, 2012.

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