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Journal articles on the topic 'Stochastic simulation'

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Published: 4 June 2021
Last updated: 8 June 2024

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1

Balmer, David, and Brian D. Ripley. "Stochastic Simulation." Journal of the Operational Research Society 40, no. 2 (February 1989): 201. http://dx.doi.org/10.2307/2583240.

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2

Nelson, Barry L., and Brian D. Ripley. "Stochastic Simulation." Journal of the American Statistical Association 84, no. 405 (March 1989): 334. http://dx.doi.org/10.2307/2289887.

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3

Morgan, B. J. T., and B. D. Ripley. "Stochastic Simulation." Biometrics 44, no. 2 (June 1988): 628. http://dx.doi.org/10.2307/2531879.

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4

Balmer, David. "Stochastic Simulation." Journal of the Operational Research Society 40, no. 2 (February 1989): 201–2. http://dx.doi.org/10.1057/jors.1989.26.

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5

Booker, Jane M. "Stochastic Simulation." Technometrics 30, no. 2 (May 1988): 231–32. http://dx.doi.org/10.1080/00401706.1988.10488373.

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6

Bongiovanni, John. "Stochastic simulation." Environmental Software 3, no. 1 (March 1988): 45. http://dx.doi.org/10.1016/0266-9838(88)90009-3.

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7

Clarke, Michael D., and Brian D. Ripley. "Stochastic Simulation." Statistician 36, no. 4 (1987): 430. http://dx.doi.org/10.2307/2348862.

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8

Junker, Brian W., and Brian D. Ripley. "Stochastic Simulation." Journal of Educational Statistics 16, no. 1 (1991): 82. http://dx.doi.org/10.2307/1165101.

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9

Kemp, C. D., and B. D. Ripley. "Stochastic Simulation." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 3 (1988): 565. http://dx.doi.org/10.2307/2983026.

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10

Mo, Wen Hui. "Monte Carlo Simulation of Reliability for Gear." Advanced Materials Research 268-270 (July 2011): 42–45. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.42.

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Production errors, material properties and applied loads of the gear are stochastic .Considering the influence of these stochastic factors, reliability of gear is studied. The sensitivity analysis of random variable can reduce the number of random variables. Simulating random variables, a lot of samples are generated. Using the Monte Carlo simulation based on the sensitivity analysis, reliabilities of contacting fatigue strength and bending fatigue strength can be obtained. The Monte Carlo simulation approaches the accurate solution gradually with the increase of the number of simulations. The numerical example validates the proposed method.
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11

Asmussen, Soren, and Gerald S. Schedler. "Regenerative Stochastic Simulation." Journal of the American Statistical Association 88, no. 424 (December 1993): 1474. http://dx.doi.org/10.2307/2291306.

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12

Ripley, B. D. "REGENERATIVE STOCHASTIC SIMULATION." Bulletin of the London Mathematical Society 26, no. 4 (July 1994): 410–11. http://dx.doi.org/10.1112/blms/26.4.410.

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13

Shaffer, Mark L., Alisa M. Shull, and Alan R. Tipton. "Stochastic Population Simulation." Conservation Biology 2, no. 1 (March 1988): 6–7. http://dx.doi.org/10.1111/j.1523-1739.1988.tb00329.x.

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14

Elkjaer, M. "Stochastic budget simulation." International Journal of Project Management 18, no. 2 (April 2000): 139–47. http://dx.doi.org/10.1016/s0263-7863(98)00078-7.

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15

Lauwens, Ben. "Stochastic hybrid simulation." 4OR 9, no. 1 (August 17, 2010): 107–10. http://dx.doi.org/10.1007/s10288-010-0135-7.

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16

Zhao, Hu, and Julia Kowalski. "Topographic uncertainty quantification for flow-like landslide models via stochastic simulations." Natural Hazards and Earth System Sciences 20, no. 5 (May 26, 2020): 1441–61. http://dx.doi.org/10.5194/nhess-20-1441-2020.

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Abstract. Digital elevation models (DEMs) representing topography are an essential input for computational models capable of simulating the run-out of flow-like landslides. Yet, DEMs are often subject to error, a fact that is mostly overlooked in landslide modeling. We address this research gap and investigate the impact of topographic uncertainty on landslide run-out models. In particular, we will describe two different approaches to account for DEM uncertainty, namely unconditional and conditional stochastic simulation methods. We investigate and discuss their feasibility, as well as whether DEM uncertainty represented by stochastic simulations critically affects landslide run-out simulations. Based upon a historic flow-like landslide event in Hong Kong, we present a series of computational scenarios to compare both methods using our modular Python-based workflow. Our results show that DEM uncertainty can significantly affect simulation-based landslide run-out analyses, depending on how well the underlying flow path is captured by the DEM, as well as on further topographic characteristics and the DEM error's variability. We further find that, in the absence of systematic bias in the DEM, a performant root-mean-square-error-based unconditional stochastic simulation yields similar results to a computationally intensive conditional stochastic simulation that takes actual DEM error values at reference locations into account. In all other cases the unconditional stochastic simulation overestimates the variability in the DEM error, which leads to an increase in the potential hazard area as well as extreme values of dynamic flow properties.
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17

Ji, Qiuyan, Feilong Han, Wei Qian, Qing Guo, and Shulin Wan. "A machine learning-driven stochastic simulation of underground sulfide distribution with multiple constraints." Open Geosciences 13, no. 1 (January 1, 2021): 807–19. http://dx.doi.org/10.1515/geo-2020-0274.

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Abstract The increase of sulfide (S2−) during the water flooding process has been regarded as an essential and potential risk for oilfield development and safety. Kriging and stochastic simulations are common methods for assessing the element distribution. However, these traditional simulation methods are not able to predict the continuous changes of underground S2− distribution in the time domain by limited known information directly. This study is a kind of attempt to combine stochastic simulation and the modified probabilistic neural network (modified PNN) for simulating short-term changes of S2− concentration. The proposed modified PNN constructs the connection between multiple indirect datasets and S2− concentration at sampling points. These connections, which are treated as indirect data in the stochastic simulation processes, is able to provide extra supports for changing the probability density function (PDF) and enhancing the stability of the simulation. In addition, the simulation process can be controlled by multiple constraints due to which the simulating target has been changed into the increment distribution of S2−. The actual data test provides S2− distributions in an oil field with good continuity and accuracy, which demonstrate the outstanding capability of this novel method.
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18

Netzer, Corinna, Michal Pasternak, Lars Seidel, Frédéric Ravet, and Fabian Mauss. "Computationally efficient prediction of cycle-to-cycle variations in spark-ignition engines." International Journal of Engine Research 21, no. 4 (June 13, 2019): 649–63. http://dx.doi.org/10.1177/1468087419856493.

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Cycle-to-cycle variations are important to consider in the development of spark-ignition engines to further increase fuel conversion efficiency. Direct numerical simulation and large eddy simulation can predict the stochastics of flows and therefore cycle-to-cycle variations. However, the computational costs are too high for engineering purposes if detailed chemistry is applied. Detailed chemistry can predict the fuels’ tendency to auto-ignite for different octane ratings as well as locally changing thermodynamic and chemical conditions which is a prerequisite for the analysis of knocking combustion. In this work, the joint use of unsteady Reynolds-averaged Navier–Stokes simulations for the analysis of the average engine cycle and the spark-ignition stochastic reactor model for the analysis of cycle-to-cycle variations is proposed. Thanks to the stochastic approach for the modeling of mixing and heat transfer, the spark-ignition stochastic reactor model can mimic the randomness of turbulent flows that is missing in the Reynolds-averaged Navier–Stokes modeling framework. The capability to predict cycle-to-cycle variations by the spark-ignition stochastic reactor model is extended by imposing two probability density functions. The probability density function for the scalar mixing time constant introduces a variation in the turbulent mixing time that is extracted from the unsteady Reynolds-averaged Navier–Stokes simulations and leads to variations in the overall mixing process. The probability density function for the inflammation time accounts for the delay or advancement of the early flame development. The combination of unsteady Reynolds-averaged Navier–Stokes and spark-ignition stochastic reactor model enables one to predict cycle-to-cycle variations using detailed chemistry in a fraction of computational time needed for a single large eddy simulation cycle.
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19

MacKeown, P. Kevin, Harvey Gould, and Jan Tobochnik. "Stochastic Simulation in Physics." American Journal of Physics 67, no. 1 (January 1999): 94–95. http://dx.doi.org/10.1119/1.19184.

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20

Breitung, K. "Ripley, B.D., Stochastic simulation." Statistical Papers 30, no. 1 (December 1989): 184. http://dx.doi.org/10.1007/bf02924321.

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21

Jauslin, H. R., and T. Schneider. "Stochastic simulation of fermions." Journal of Statistical Physics 43, no. 5-6 (June 1986): 865–68. http://dx.doi.org/10.1007/bf02628312.

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22

Chen, Chun-Hung, Leyuan Shi, and Loo Hay Lee. "Stochastic systems simulation optimization." Frontiers of Electrical and Electronic Engineering in China 6, no. 3 (September 2011): 468–80. http://dx.doi.org/10.1007/s11460-011-0168-5.

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23

Sykes, Alan M., and Alan J. Watkins. "Stochastic simulation and APL." ACM SIGAPL APL Quote Quad 20, no. 3 (March 1990): 8–13. http://dx.doi.org/10.1145/379206.379213.

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24

Bulik, M., M. Liefvendahl, R. Stocki, and C. Wauquiez. "Stochastic simulation for crashworthiness." Advances in Engineering Software 35, no. 12 (December 2004): 791–803. http://dx.doi.org/10.1016/j.advengsoft.2004.07.002.

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25

Breipohl, Arthur M., Fred N. Lee, and Jia-Yo Chiang. "Stochastic production cost simulation." Reliability Engineering & System Safety 46, no. 1 (January 1994): 101–7. http://dx.doi.org/10.1016/0951-8320(94)90052-3.

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26

Näf, Urs G. "Stochastic simulation using gPROMS." Computers & Chemical Engineering 18 (January 1994): S743—S747. http://dx.doi.org/10.1016/0098-1354(94)80121-5.

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27

Dolinska, M. E., and N. L. Doroshko. "Multiscale simulation algorithm for stochastic cooling simulation." Nuclear Physics and Atomic Energy 17, no. 4 (December 25, 2016): 406–10. http://dx.doi.org/10.15407/jnpae2016.04.406.

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28

Herdiana, Ratna. "NUMERICAL SIMULATION OF STOCHASTIC DIFFERENTIAL EQUATIONS USING IMPLICIT MILSTEIN METHOD." Journal of Fundamental Mathematics and Applications (JFMA) 3, no. 1 (June 10, 2020): 72–83. http://dx.doi.org/10.14710/jfma.v3i1.7416.

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Stiff stochastic differential equations arise in many applications including in the area of biology. In this paper, we present numerical solution of stochastic differential equations representing the Malthus population model and SIS epidemic model, using the improved implicit Milstein method of order one proposed in [6]. The open source programming language SCILAB is used to perform the numerical simulations. Results show that the method is more accurate and stable compared to the implicit Euler method.
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29

Fu, Xiaodi, Xiaoyan He, and Liuqian Ding. "Stochastic Flood Simulation Method Combining Flood Intensity and Morphological Indicators." Sustainability 15, no. 18 (September 21, 2023): 14032. http://dx.doi.org/10.3390/su151814032.

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The existing flood stochastic simulation methods are mostly applied to the stochastic simulation of flood intensity characteristics, with less consideration for the randomness of the flood hydrograph shape and its correlation with intensity characteristics. In view of this, this paper proposes a flood stochastic simulation method that combines intensity and morphological indicators. Using the Foziling and Xianghongdian reservoirs in the Pi River basin in China as examples, this method utilizes a three-dimensional asymmetric Archimedean M6 Copula to construct stochastic simulation models for peak flow, flood volume, and flood duration. Based on K-means clustering, a multivariate Gaussian Copula is employed to construct a dimensionless flood hydrograph stochastic simulation model. Furthermore, separate two-dimensional symmetric Copula stochastic simulation models are established to capture the correlations between flood intensity characteristics and shape variables such as peak shape coefficient, peak occurrence time, rising inflection point angle, and coefficient of variation. By evaluating the fit between the simulated flood characteristics and the dimensionless flood hydrograph, a complete flood hydrograph is synthesized, which can be applied in flood control dispatch simulations and other related fields. The feasibility and practicality of the proposed model are analyzed and demonstrated. The results indicate that the simulated floods closely resemble natural floods, making the simulation outcomes crucial for reservoir scheduling, risk assessment, and decision-making processes.
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30

Zhang, Lanxin, Junyu Wang, and Max von Kleist. "Numerical approaches for the rapid analysis of prophylactic efficacy against HIV with arbitrary drug-dosing schemes." PLOS Computational Biology 17, no. 12 (December 23, 2021): e1009295. http://dx.doi.org/10.1371/journal.pcbi.1009295.

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Pre-exposure prophylaxis (PrEP) is an important pillar to prevent HIV transmission. Because of experimental and clinical shortcomings, mathematical models that integrate pharmacological, viral- and host factors are frequently used to quantify clinical efficacy of PrEP. Stochastic simulations of these models provides sample statistics from which the clinical efficacy is approximated. However, many stochastic simulations are needed to reduce the associated sampling error. To remedy the shortcomings of stochastic simulation, we developed a numerical method that allows predicting the efficacy of arbitrary prophylactic regimen directly from a viral dynamics model, without sampling. We apply the method to various hypothetical dolutegravir (DTG) prophylaxis scenarios. The approach is verified against state-of-the-art stochastic simulation. While the method is more accurate than stochastic simulation, it is superior in terms of computational performance. For example, a continuous 6-month prophylactic profile is computed within a few seconds on a laptop computer. The method’s computational performance, therefore, substantially expands the horizon of feasible analysis in the context of PrEP, and possibly other applications.
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31

Stutz, Timothy C., Alfonso Landeros, Jason Xu, Janet S. Sinsheimer, Mary Sehl, and Kenneth Lange. "Stochastic simulation algorithms for Interacting Particle Systems." PLOS ONE 16, no. 3 (March 2, 2021): e0247046. http://dx.doi.org/10.1371/journal.pone.0247046.

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Interacting Particle Systems (IPSs) are used to model spatio-temporal stochastic systems in many disparate areas of science. We design an algorithmic framework that reduces IPS simulation to simulation of well-mixed Chemical Reaction Networks (CRNs). This framework minimizes the number of associated reaction channels and decouples the computational cost of the simulations from the size of the lattice. Decoupling allows our software to make use of a wide class of techniques typically reserved for well-mixed CRNs. We implement the direct stochastic simulation algorithm in the open source programming language Julia. We also apply our algorithms to several complex spatial stochastic phenomena. including a rock-paper-scissors game, cancer growth in response to immunotherapy, and lipid oxidation dynamics. Our approach aids in standardizing mathematical models and in generating hypotheses based on concrete mechanistic behavior across a wide range of observed spatial phenomena.
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32

Koerkamp, Bas Groot, Theo Stijnen, Milton C. Weinstein, and M. G. Myriam Hunink. "The Combined Analysis of Uncertainty and Patient Heterogeneity in Medical Decision Models." Medical Decision Making 31, no. 4 (October 25, 2010): 650–61. http://dx.doi.org/10.1177/0272989x10381282.

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The analysis of both patient heterogeneity and parameter uncertainty in decision models is increasingly recommended. In addition, the complexity of current medical decision models commonly requires simulating individual subjects, which introduces stochastic uncertainty. The combined analysis of uncertainty and heterogeneity often involves complex nested Monte Carlo simulations to obtain the model outcomes of interest. In this article, the authors distinguish eight model types, each dealing with a different combination of patient heterogeneity, parameter uncertainty, and stochastic uncertainty. The analyses that are required to obtain the model outcomes are expressed in equations, explained in stepwise algorithms, and demonstrated in examples. Patient heterogeneity is represented by frequency distributions and analyzed with Monte Carlo simulation. Parameter uncertainty is represented by probability distributions and analyzed with 2nd-order Monte Carlo simulation (aka probabilistic sensitivity analysis). Stochastic uncertainty is analyzed with 1st-order Monte Carlo simulation (i.e., trials or random walks). This article can be used as a reference for analyzing complex models with more than one type of uncertainty and patient heterogeneity.
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33

Hedar, Abdel-Rahman, Amira Allam, and Alaa Abdel-Hakim. "Simulation-Based EDAs for Stochastic Programming Problems." Computation 8, no. 1 (March 18, 2020): 18. http://dx.doi.org/10.3390/computation8010018.

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With the rapid growth of simulation software packages, generating practical tools for simulation-based optimization has attracted a lot of interest over the last decades. In this paper, a modified method of Estimation of Distribution Algorithms (EDAs) is constructed by a combination with variable-sample techniques to deal with simulation-based optimization problems. Moreover, a new variable-sample technique is introduced to support the search process whenever the sample sizes are small, especially in the beginning of the search process. The proposed method shows efficient results by simulating several numerical experiments.
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34

Scholz, Klaus. "Stochastic simulation of urbanhydrological processes." Water Science and Technology 36, no. 8-9 (October 1, 1997): 25–31. http://dx.doi.org/10.2166/wst.1997.0639.

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Calculations in urban hydrology have almost exclusively been of deterministic character and give therefore unequivocal results. Uncertainties, which are always present, can not been eliminated by more complex models. To take uncertainties into account stochastic algorithms are integrated into hydrological components. A stochastic-hydrological method has developed which can be used to various problems. In contrast to the usual purely deterministic models the model makes it possible to get concrete information of liability of the calibration and prognosis regarding confidence limits The model is applied for the calibration and prognosis of pollutant load hydrographs. The result is, that stochastic and physical based parameters should be taken into account.
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35

Abogrean, Elbahlul Musa. "Stochastic Simulation of Machine Breakdown." Journal of Public Administration and Governance 2, no. 1 (February 12, 2012): 95. http://dx.doi.org/10.5296/jpag.v2i1.1285.

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This paper explores the value of stochastic simulation as a tool for predicting current or future reliability of machinery or component parts, it helps to enhance and certify more realistic results by increasing the confidence levels by carrying out replications and seeing the changes that occur. A simulation model has been constructed based on theoretical fundamentals has been developed which support the creation of the main elements for building and implementing stochastic models accurately. It represents the processes of a machine that has three parameters; namely those are drill head, dusting and lubrication. The consumption of these parameters results in the development of a probability of failure for the machine that can be validated by the Hugin software.
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36

Choe, Ri, Tae-Jin Park, and Kwang-Ryel Ryu. "AGV Dispatching with Stochastic Simulation." Journal of Korean navigation and port research 32, no. 10 (December 31, 2008): 837–44. http://dx.doi.org/10.5394/kinpr.2008.32.10.837.

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37

Lee, P. M., and Byron S. Gottfried. "Elements of Stochastic Process Simulation." Mathematical Gazette 69, no. 447 (March 1985): 64. http://dx.doi.org/10.2307/3616475.

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38

Bell, Peter C. "Stochastic Visual Interactive Simulation Models." Journal of the Operational Research Society 40, no. 7 (July 1989): 615. http://dx.doi.org/10.2307/2582970.

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39

Arutyunyan, R. V. "Stochastic Simulation of Diffusion Filtering." Izvestiya of Saratov University. New Series. Series: Mathematics. Mechanics. Informatics 16, no. 1 (2016): 5–12. http://dx.doi.org/10.18500/1816-9791-2016-16-1-5-12.

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40

Luvsantseren, Purevdolgor, Enkhbayar Purevjav, and Khenmedeh Lochin. "Stochastic simulation of cell cycle." Advanced Studies in Biology 5 (2013): 1–9. http://dx.doi.org/10.12988/asb.2013.13001.

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41

Van Segbroeck, Sven, Ann Nowé, and Tom Lenaerts. "Stochastic Simulation of the Chemoton." Artificial Life 15, no. 2 (April 2009): 213–26. http://dx.doi.org/10.1162/artl.2009.15.2.15203.

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Gánti's chemoton model is an illustrious example of a minimal cell model. It is composed of three stoichiometrically coupled autocatalytic subsystems: a metabolism, a template replication process, and a membrane enclosing the other two. Earlier studies on chemoton dynamics yield inconsistent results. Furthermore, they all appealed to deterministic simulations, which do not take into account the stochastic effects induced by small population sizes. We present, for the first time, results of a chemoton simulation in which these stochastic effects have been taken into account. We investigate the dynamics of the system and analyze in depth the mechanisms responsible for the observed behavior. Our results suggest that, in contrast to the most recent study by Munteanu and Solé, the stochastic chemoton reaches a unique stable division time after a short transient phase. We confirm the existence of an optimal template length and show that this is a consequence of the monomer concentration, which depends on the template length and the initiation threshold. Since longer templates imply shorter division times, these results motivate the selective pressure toward longer templates observed in nature.
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42

Di Ge, E. Le Carpentier, Jérôme Idier, and Dario Farina. "Spike Sorting by Stochastic Simulation." IEEE Transactions on Neural Systems and Rehabilitation Engineering 19, no. 3 (June 2011): 249–59. http://dx.doi.org/10.1109/tnsre.2011.2112780.

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43

Moutoussamy, Vincent, Simon Nanty, and Benoît Pauwels. "Emulators for stochastic simulation codes." ESAIM: Proceedings and Surveys 48 (January 2015): 116–55. http://dx.doi.org/10.1051/proc/201448005.

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44

Satchell, J. "Stochastic simulation of SFQ logic." IEEE Transactions on Appiled Superconductivity 7, no. 2 (June 1997): 3315–18. http://dx.doi.org/10.1109/77.622070.

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45

Ding, L. Y., R. K. Mehra, and J. K. Donnelly. "Stochastic Modeling in Reservoir Simulation." SPE Reservoir Engineering 7, no. 01 (February 1, 1992): 98–106. http://dx.doi.org/10.2118/18431-pa.

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46

Kozachenko, Yuri, and Iryna Rozora. "Simulation of Gaussian stochastic processes." Random Operators and Stochastic Equations 11, no. 3 (September 1, 2003): 275–96. http://dx.doi.org/10.1163/156939703771378626.

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47

Duso, Lorenzo, and Christoph Zechner. "Selected-node stochastic simulation algorithm." Journal of Chemical Physics 148, no. 16 (April 28, 2018): 164108. http://dx.doi.org/10.1063/1.5021242.

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48

Catterall, S. M., I. T. Drummond, and R. R. Horgan. "Stochastic simulation of quantum mechanics." Journal of Physics A: Mathematical and General 24, no. 17 (September 7, 1991): 4081–91. http://dx.doi.org/10.1088/0305-4470/24/17/025.

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49

Ankenman, Bruce, Barry L. Nelson, and Jeremy Staum. "Stochastic Kriging for Simulation Metamodeling." Operations Research 58, no. 2 (April 2010): 371–82. http://dx.doi.org/10.1287/opre.1090.0754.

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50

Bell, Peter C. "Stochastic Visual Interactive Simulation Models." Journal of the Operational Research Society 40, no. 7 (July 1989): 615–24. http://dx.doi.org/10.1057/jors.1989.104.

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