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Artykuły w czasopismach na temat "Stochastic simulation"
Balmer, David, i Brian D. Ripley. "Stochastic Simulation". Journal of the Operational Research Society 40, nr 2 (luty 1989): 201. http://dx.doi.org/10.2307/2583240.
Pełny tekst źródłaNelson, Barry L., i Brian D. Ripley. "Stochastic Simulation." Journal of the American Statistical Association 84, nr 405 (marzec 1989): 334. http://dx.doi.org/10.2307/2289887.
Pełny tekst źródłaMorgan, B. J. T., i B. D. Ripley. "Stochastic Simulation." Biometrics 44, nr 2 (czerwiec 1988): 628. http://dx.doi.org/10.2307/2531879.
Pełny tekst źródłaBalmer, David. "Stochastic Simulation". Journal of the Operational Research Society 40, nr 2 (luty 1989): 201–2. http://dx.doi.org/10.1057/jors.1989.26.
Pełny tekst źródłaBooker, Jane M. "Stochastic Simulation". Technometrics 30, nr 2 (maj 1988): 231–32. http://dx.doi.org/10.1080/00401706.1988.10488373.
Pełny tekst źródłaBongiovanni, John. "Stochastic simulation". Environmental Software 3, nr 1 (marzec 1988): 45. http://dx.doi.org/10.1016/0266-9838(88)90009-3.
Pełny tekst źródłaClarke, Michael D., i Brian D. Ripley. "Stochastic Simulation." Statistician 36, nr 4 (1987): 430. http://dx.doi.org/10.2307/2348862.
Pełny tekst źródłaJunker, Brian W., i Brian D. Ripley. "Stochastic Simulation". Journal of Educational Statistics 16, nr 1 (1991): 82. http://dx.doi.org/10.2307/1165101.
Pełny tekst źródłaKemp, C. D., i B. D. Ripley. "Stochastic Simulation." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, nr 3 (1988): 565. http://dx.doi.org/10.2307/2983026.
Pełny tekst źródłaMo, Wen Hui. "Monte Carlo Simulation of Reliability for Gear". Advanced Materials Research 268-270 (lipiec 2011): 42–45. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.42.
Pełny tekst źródłaRozprawy doktorskie na temat "Stochastic simulation"
Hellander, Stefan. "Stochastic Simulation of Reaction-Diffusion Processes". Doctoral thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-198522.
Pełny tekst źródłaeSSENCE
Drawert, Brian J. "Spatial Stochastic Simulation of Biochemical Systems". Thesis, University of California, Santa Barbara, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3559784.
Pełny tekst źródłaRecent advances in biology have shown that proteins and genes often interact probabilistically. The resulting effects that arise from these stochastic dynamics differ significantly than traditional deterministic formulations, and have biologically significant ramifications. This has led to the development of computational models of the discrete stochastic biochemical pathways found in living organisms. These include spatial stochastic models, where the physical extent of the domain plays an important role; analogous to traditional partial differential equations.
Simulation of spatial stochastic models is a computationally intensive task. We have developed a new algorithm, the Diffusive Finite State Projection (DFSP) method for the efficient and accurate simulation of stochastic spatially inhomogeneous biochemical systems. DFSP makes use of a novel formulation of Finite State Projection (FSP) to simulate diffusion, while reactions are handled by the Stochastic Simulation Algorithm (SSA). Further, we adapt DFSP to three dimensional, unstructured, tetrahedral meshes in inclusion in the mature and widely usable systems biology modeling software URDME, enabling simulation of the complex geometries found in biological systems. Additionally, we extend DFSP with adaptive error control and a highly efficient parallel implementation for the graphics processing units (GPU).
In an effort to understand biological processes that exhibit stochastic dynamics, we have developed a spatial stochastic model of cellular polarization. Specifically we investigate the ability of yeast cells to sense a spatial gradient of mating pheromone and respond by forming a projection in the direction of the mating partner. Our results demonstrates that higher levels of stochastic noise results in increased robustness, giving support to a cellular model where noise and spatial heterogeneity combine to achieve robust biological function. This also highlights the importance of spatial stochastic modeling to reproduce experimental observations.
Homem, de Mello Tito. "Simulation-based methods for stochastic optimization". Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/24846.
Pełny tekst źródłaMorton-Firth, Carl Jason. "Stochastic simulation of cell signalling pathways". Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625063.
Pełny tekst źródłaCheung, Ricky. "Stochastic based football simulation using data". Thesis, Uppsala universitet, Matematiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-359835.
Pełny tekst źródłaDu, Manuel. "Stochastic simulation studies for honeybee breeding". Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22295.
Pełny tekst źródłaThe present work describes a stochastic simulation program for modelling honeybee populations under breeding conditions. The program was newly implemented to investigate and optimize different selection strategies. A first study evaluated in how far the program's predictions depend on the underlying genetic model. It was found that the finite locus model rather than the infinitesimal model should be used for long-term investigations. A second study shed light into the importance of controlled mating for honeybee breeding. It was found that breeding schemes with controlled mating are far superior to free-mating alternatives. Ultimately, a final study examined how successful breeding strategies can be designed so that they are sustainable in the long term. For this, short-term genetic progress has to be weighed against the avoidance of inbreeding in the long run. By extensive simulations, optimal selection intensities on the maternal and paternal paths could be determined for different sets of population parameters.
Vasan, Arunchandar. "Timestepped stochastic simulation of 802.11 WLANs". College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8533.
Pełny tekst źródłaThesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Hashemi, Fatemeh Sadat. "Sampling Controlled Stochastic Recursions: Applications to Simulation Optimization and Stochastic Root Finding". Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/76740.
Pełny tekst źródłaPh. D.
Albertyn, Martin. "Generic simulation modelling of stochastic continuous systems". Thesis, Pretoria : [s.n.], 2004. http://upetd.up.ac.za/thesis/available/etd-05242005-112442.
Pełny tekst źródłaXu, Zhouyi. "Stochastic Modeling and Simulation of Gene Networks". Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/645.
Pełny tekst źródłaKsiążki na temat "Stochastic simulation"
Stochastic simulation. New York: Wiley, 1987.
Znajdź pełny tekst źródłaRipley, Brian D., red. Stochastic Simulation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1987. http://dx.doi.org/10.1002/9780470316726.
Pełny tekst źródłaShedler, G. S. Regenerative stochastic simulation. Boston: Academic Press, 1993.
Znajdź pełny tekst źródłasentralbyrå, Norway Statistisk, red. Stochastic simulation of KVARTS91. Oslo: Statistisk sentralbyrå, 1993.
Znajdź pełny tekst źródłaMacKeown, P. K. Stochastic simulation in physics. New York: Springer, 1997.
Znajdź pełny tekst źródłaNelson, Barry L. Stochastic modeling: Analysis & simulation. Mineloa, N.Y: Dover Publications, 2002.
Znajdź pełny tekst źródłaBalakrishnan, N., V. B. Melas i S. Ermakov, red. Advances in Stochastic Simulation Methods. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1318-5.
Pełny tekst źródłaAsmussen, Søren, i 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.
Pełny tekst źródłaStochastic modeling: Analysis and simulation. New York: McGraw-Hill, 1995.
Znajdź pełny tekst źródłaNelson, Barry L. Stochastic modeling: Analysis and simulation. Mineola, N.Y: Dover Publications, 2010.
Znajdź pełny tekst źródłaCzęści książek na temat "Stochastic simulation"
Drew, John H., Diane L. Evans, Andrew G. Glen i Lawrence M. Leemis. "Stochastic Simulation". W International Series in Operations Research & Management Science, 209–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43323-3_12.
Pełny tekst źródłaOlea, Ricardo A. "Stochastic Simulation". W Geostatistics for Engineers and Earth Scientists, 141–62. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5001-3_9.
Pełny tekst źródłaHeermann, Dieter W. "Stochastic Simulation". W Encyclopedia of Applied and Computational Mathematics, 1402–4. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-540-70529-1_557.
Pełny tekst źródłaBerlinger, Marcel. "Stochastic Simulation". W A Methodology to Model the Statistical Fracture Behavior of Acrylic Glasses for Stochastic Simulation, 93–109. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-34330-9_7.
Pełny tekst źródłaRhinehart, R. Russell, i Robert M. Bethea. "Stochastic Simulation". W Applied Engineering Statistics, 189–204. Wyd. 2. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003222330-10.
Pełny tekst źródłaRomero, Paulo, i Martins Maciel. "Stochastic Simulation". W Performance, Reliability, and Availability Evaluation of Computational Systems, Volume I, 705–86. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003306016-14.
Pełny tekst źródłaLantuéjoul, Christian. "Investigating stochastic models". W Geostatistical Simulation, 9–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04808-5_2.
Pełny tekst źródłaGrigoriu, Mircea. "Monte Carlo Simulation". W Stochastic Calculus, 287–342. Boston, MA: Birkhäuser Boston, 2002. http://dx.doi.org/10.1007/978-0-8176-8228-6_5.
Pełny tekst źródłaHaas, Peter J. "Regenerative Simulation". W Stochastic Petri Nets, 189–273. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/0-387-21552-2_6.
Pełny tekst źródłaAndò, Bruno, i Salvatore Graziani. "The Nass Simulation Environment". W Stochastic Resonance, 177–86. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4391-6_6.
Pełny tekst źródłaStreszczenia konferencji na temat "Stochastic simulation"
Apaydin, Mehmet Serkan, Douglas L. Brutlag, Carlos Guestrin, David Hsu i Jean-Claude Latombe. "Stochastic roadmap simulation". W the sixth annual international conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/565196.565199.
Pełny tekst źródłaGhosh, Soumyadip, i Henry Lam. "Mirror descent stochastic approximation for computing worst-case stochastic input models". W 2015 Winter Simulation Conference (WSC). IEEE, 2015. http://dx.doi.org/10.1109/wsc.2015.7408184.
Pełny tekst źródłaPasserat-Palmbach, Jonathan, Jonathan Caux, Yannick Le Pennec, Romain Reuillon, Ivan Junier, François Kepes i David R. C. Hill. "Parallel stepwise stochastic simulation". W the 2013 ACM SIGSIM conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2486092.2486114.
Pełny tekst źródłaDinh, Cong Que, Seiji Nagahara, Gousuke Shiraishi, Yukie Minekawa, Yuya Kamei, Michael Carcasi, Hiroyuki Ide i in. "Calibrated PSCAR stochastic simulation". W Extreme Ultraviolet (EUV) Lithography X, redaktor Kenneth A. Goldberg. SPIE, 2019. http://dx.doi.org/10.1117/12.2515183.
Pełny tekst źródłaBalbo, G., i G. Chiola. "Stochastic petri net simulation". W the 21st conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/76738.76772.
Pełny tekst źródłaPlumlee, Matthew, i Henry Lam. "Learning stochastic model discrepancy". W 2016 Winter Simulation Conference (WSC). IEEE, 2016. http://dx.doi.org/10.1109/wsc.2016.7822108.
Pełny tekst źródłaShanbhag, Uday V., i Jose H. Blanchet. "Budget-constrained stochastic approximation". W 2015 Winter Simulation Conference (WSC). IEEE, 2015. http://dx.doi.org/10.1109/wsc.2015.7408179.
Pełny tekst źródłaDain, Oliver, Matthew Ginsberg, Erin Keenan, John Pyle, Tristan Smith, Andrew Stoneman i Iain Pardoe. "Stochastic Shipyard Simulation with Simyard". W 2006 Winter Simulation Conference. IEEE, 2006. http://dx.doi.org/10.1109/wsc.2006.322954.
Pełny tekst źródłaYousefian, Farzad, Angelia Nedic i Uday V. Shanbhag. "A smoothing stochastic quasi-newton method for non-lipschitzian stochastic optimization problems". W 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8247960.
Pełny tekst źródłaAnkenman, Bruce, Barry L. Nelson i Jeremy Staum. "Stochastic kriging for simulation metamodeling". W 2008 Winter Simulation Conference (WSC). IEEE, 2008. http://dx.doi.org/10.1109/wsc.2008.4736089.
Pełny tekst źródłaRaporty organizacyjne na temat "Stochastic simulation"
Siebke, Christian, Maximilian Bäumler, Madlen Ringhand, Marcus Mai, Felix Elrod i Günther Prokop. Report on design of modules for the stochastic traffic simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.245.
Pełny tekst źródłaBäumler, Maximilian, Madlen Ringhand, Christian Siebke, Marcus Mai, Felix Elrod i Günther Prokop. Report on validation of the stochastic traffic simulation (Part B). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.243.
Pełny tekst źródłaJames Glimm i Xiaolin Li. Multiscale Stochastic Simulation and Modeling. Office of Scientific and Technical Information (OSTI), styczeń 2006. http://dx.doi.org/10.2172/862194.
Pełny tekst źródłaField, Richard V. ,. Jr. Stochastic models: theory and simulation. Office of Scientific and Technical Information (OSTI), marzec 2008. http://dx.doi.org/10.2172/932886.
Pełny tekst źródłaRinghand, Madlen, Maximilian Bäumler, Christian Siebke, Marcus Mai i Felix Elrod. Report on validation of the stochastic traffic simulation (Part A). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.242.
Pełny tekst źródłaSiebke, Christian, Maximilian Bäumler, Madlen Ringhand, Marcus Mai, Felix Elrod i Günther Prokop. Report on integration of the stochastic traffic simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.246.
Pełny tekst źródłaGlynn, Peter W. Optimization of Stochastic Systems via Simulation. Fort Belvoir, VA: Defense Technical Information Center, sierpień 1989. http://dx.doi.org/10.21236/ada214011.
Pełny tekst źródłaJohnson, Ralph. Stochastic Simulation Analysis - 2005 (SSA-05). Fort Belvoir, VA: Defense Technical Information Center, lipiec 1997. http://dx.doi.org/10.21236/ada329429.
Pełny tekst źródłaFricks, John, i Gustavo Didier. Statistical Inference and Stochastic Simulation for Microrheology. Fort Belvoir, VA: Defense Technical Information Center, grudzień 2013. http://dx.doi.org/10.21236/ada605473.
Pełny tekst źródłaHeinisch, H. L. Stochastic annealing simulation of cascades in metals. Office of Scientific and Technical Information (OSTI), kwiecień 1996. http://dx.doi.org/10.2172/270462.
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