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Books on the topic 'Stochastic simulation algorithms'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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1

Asmussen, Søren, and Peter W. Glynn. Stochastic Simulation: Algorithms and Analysis. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-69033-9.

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2

Asmussen, Søren. Stochastic simulation: Algorithms and analysis. New York: Springer, 2011.

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3

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

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4

Chang, Hyeong Soo. Simulation-Based Algorithms for Markov Decision Processes. 2nd ed. London: Springer London, 2013.

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5

Judd, Kenneth L. One-node quadrature beats monte carlo: A generalized stochastic simulation algorithm. Cambridge, MA: National Bureau of Economic Research, 2011.

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6

Shi, Yixi. Rare Events in Stochastic Systems: Modeling, Simulation Design and Algorithm Analysis. [New York, N.Y.?]: [publisher not identified], 2013.

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7

Dieter, Fiems, Vincent Jean-Marc, and SpringerLink (Online service), eds. Analytical and Stochastic Modeling Techniques and Applications: 19th International Conference, ASMTA 2012, Grenoble, France, June 4-6, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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8

Glynn, Peter W., and Søren Asmussen. Stochastic Simulation: Algorithms and Analysis. Springer London, Limited, 2007.

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9

Stochastic Simulation: Algorithms and Analysis (Stochastic Modelling and Applied Probability). Springer, 2007.

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10

Öttinger, Hans C. Stochastic Processes in Polymeric Fluids: Tools and Examples for Developing Simulation Algorithms. Springer, 1995.

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11

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

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12

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

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13

Chang, Hyeong Soo, Michael C. Fu, and Jiaqiao Hu. Simulation-Based Algorithms for Markov Decision Processes. Springer, 2013.

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14

Chang, Hyeong Soo, Michael C. Fu, Jiaqiao Hu, and Steven I. Marcus. Simulation-Based Algorithms for Markov Decision Processes. Springer London, Limited, 2010.

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15

Chang, Hyeong Soo, Michael C. Fu, and Jiaqiao Hu. Simulation-Based Algorithms for Markov Decision Processes. Springer London, Limited, 2015.

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16

Simulation-Based Algorithms for Markov Decision Processes. Springer London, Limited, 2013.

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17

Allen, Michael P., and Dominic J. Tildesley. Molecular dynamics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0003.

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This chapter introduces the classical equations of motion for a system of molecules, and describes their solution by stable, accurate, time-stepping algorithms. Simple atomic systems, rigid molecules, and flexible molecules with and without constraints, are treated, with examples of program code. Quaternions are introduced as useful parameters for solving the rigid-body equations of motion of molecules. A simple example of a multiple timestep algorithm is given, and there is a brief summary of event-driven (hard-particle) dynamics. Examples of constant-temperature molecular dynamics using stochastic and deterministic methods are presented, and the corresponding constant-pressure molecular dynamics methods for fixed and variable box-shape are described. The molecular dynamics method is extended to the treatment of polarizable systems, and dynamical simulation of the grand canonical ensemble is mentioned.
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18

Simulating Copulas Stochastic Models Sampling Algorithms And Applications. Imperial College Press, 2012.

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19

Scherer, Matthias, and Jan-Frederik Mai. Simulating Copulas: Stochastic Models, Sampling Algorithms and Applications. World Scientific Publishing Co Pte Ltd, 2017.

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20

Simulating Copulas: Stochastic Models, Sampling Algorithms and Applications. World Scientific Publishing Co Pte Ltd, 2017.

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21

Bao, Yun, Carl Chiarella, and Boda Kang. Particle Filters for Markov-Switching Stochastic Volatility Models. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.9.

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This chapter proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed by a first-order Markov chain. It proposes an ongoing updated Dirichlet distribution to estimate the transition probabilities of the Markov chain in the auxiliary particle filter. A simulation-based algorithm is presented for the method that demonstrates the ability to estimate a class of models in which the probability that the system state transits from one regime to a different regime is relatively high. The methodology is implemented in order to analyze a real-time series, namely, the foreign exchange rate between the Australian dollar and the South Korean won.
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22

Martinez, Josue G., and Dennis D. Cox. Investigation of the tau-leap method for stochastic simulation: The link between the tau-leap method and the stochastic simulation algorithm. VDM Verlag Dr. Müller, 2010.

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23

Heindl, Armin, Khalid Al-Begain, and Miklos Telek. Analytical and Stochastic Modeling Techniques and Applications: 15th International Conference, ASMTA 2008 Nicosia, Cyprus, June 4-6, 2008 Proceedings. Springer London, Limited, 2008.

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24

Al-Begain, Khalid, Dieter Fiems, and William Knottenbelt. Analytical and Stochastic Modeling Techniques and Applications: 17th International Conference, ASMTA 2010, Cardiff, UK, June 14-16, 2010, Proceedings. Springer London, Limited, 2010.

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