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Artykuły w czasopismach na temat "Stochastic weights"
Xiong, Fenfen, Wei Chen, Ying Xiong i Shuxing Yang. "Weighted stochastic response surface method considering sample weights". Structural and Multidisciplinary Optimization 43, nr 6 (3.02.2011): 837–49. http://dx.doi.org/10.1007/s00158-011-0621-3.
Pełny tekst źródłaLi, Yan, i Yi Shen. "Preserving Global Exponential Stability of Hybrid BAM Neural Networks with Reaction Diffusion Terms in the Presence of Stochastic Noise and Connection Weight Matrices Uncertainty". Mathematical Problems in Engineering 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/486052.
Pełny tekst źródłaGoldenberg, David H. "Beta Instability and Stochastic Market Weights". Management Science 31, nr 4 (kwiecień 1985): 415–21. http://dx.doi.org/10.1287/mnsc.31.4.415.
Pełny tekst źródłaCaraballo, Luis E., Pablo Pérez-Lantero, Carlos Seara i Inmaculada Ventura. "Maximum Box Problem on Stochastic Points". Algorithmica 83, nr 12 (28.10.2021): 3741–65. http://dx.doi.org/10.1007/s00453-021-00882-z.
Pełny tekst źródłaYang, Zao-li, i Lu-cheng Huang. "Dynamic Stochastic Multiattribute Decision-Making That Considers Stochastic Variable Variance Characteristics under Time-Sequence Contingency Environments". Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/7126856.
Pełny tekst źródłaGashi, Bujar. "Optimal stochastic regulators with state-dependent weights". Systems & Control Letters 134 (grudzień 2019): 104522. http://dx.doi.org/10.1016/j.sysconle.2019.104522.
Pełny tekst źródłaXu, Liyan, Tony Vladusich, Fabing Duan, Lachlan J. Gunn, Derek Abbott i Mark D. McDonnell. "Decoding suprathreshold stochastic resonance with optimal weights". Physics Letters A 379, nr 38 (październik 2015): 2277–83. http://dx.doi.org/10.1016/j.physleta.2015.05.032.
Pełny tekst źródłaBrüggemann, Ralf, i Helmut Lütkepohl. "Forecasting contemporaneous aggregates with stochastic aggregation weights". International Journal of Forecasting 29, nr 1 (styczeń 2013): 60–68. http://dx.doi.org/10.1016/j.ijforecast.2012.05.007.
Pełny tekst źródłaLiu, Qingliang, i Jinmei Lai. "Stochastic Loss Function". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 04 (3.04.2020): 4884–91. http://dx.doi.org/10.1609/aaai.v34i04.5925.
Pełny tekst źródłaGuo, Hao, Jiyong Jin i Bin Liu. "Stochastic Weight Averaging Revisited". Applied Sciences 13, nr 5 (24.02.2023): 2935. http://dx.doi.org/10.3390/app13052935.
Pełny tekst źródłaRozprawy doktorskie na temat "Stochastic weights"
Dohndorf, Iryna [Verfasser], Peter [Akademischer Betreuer] Buchholz i Boudewijn R. [Gutachter] Haverkort. "Stochastic graph models with phase type distributed edge weights / Iryna Dohndorf ; Gutachter: Boudewijn R. Haverkort ; Betreuer: Peter Buchholz". Dortmund : Universitätsbibliothek Dortmund, 2017. http://d-nb.info/1134953046/34.
Pełny tekst źródłaMedeiros, Júnior Maurício da Silva. "Stochastic discount factor bounds and rare events: a review". reponame:Repositório Institucional do FGV, 2016. http://hdl.handle.net/10438/16459.
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We aim to provide a review of the stochastic discount factor bounds usually applied to diagnose asset pricing models. In particular, we mainly discuss the bounds used to analyze the disaster model of Barro (2006). Our attention is focused in this disaster model since the stochastic discount factor bounds that are applied to study the performance of disaster models usually consider the approach of Barro (2006). We first present the entropy bounds that provide a diagnosis of the analyzed disaster model which are the methods of Almeida and Garcia (2012, 2016); Ghosh et al. (2016). Then, we discuss how their results according to the disaster model are related to each other and also present the findings of other methodologies that are similar to these bounds but provide different evidence about the performance of the framework developed by Barro (2006).
Silva, Emanuel Araújo. "MODELAGEM DINÂMICA PARA SIMULAÇÃO NO PROCESSO DE ARENIZAÇÃO E COBERTURA FLORESTAL NA CAMPANHA OCIDENTAL - RS". Universidade Federal de Santa Maria, 2015. http://repositorio.ufsm.br/handle/1/3781.
Pełny tekst źródłaA modelagem dinâmica é uma ferramenta útil para o conhecimento do uso e ocupação da terra, gerando diretrizes metodológicas associadas às questões ambientais, sociais e econômicas. Este trabalho teve por objetivo aplicar um modelo para simular a dinâmica no processo de arenização e cobertura florestal do Sudoeste do Rio Grande do Sul, denominada microrregião da Campanha Ocidental e, com base nessas técnicas, efetuar a projeção de cenários futuros. Foi utilizado um mosaico de imagens do satélite LANDSAT 5 sensor TM, que recobre a região de estudo nos anos de 1985, 1996 e 2011 e LANDSAT 8 sensor OLI no ano de 2013. Para elaboração da base de dados e processamento digital das imagens, utilizou-se o aplicativo SPRING. Após a classificação das imagens, foi realizado o cruzamento dos mapas temáticos com auxílio da programação LEGAL, e posteriormente, empregado a simulação dos cenários futuros por meio da modelagem com o aplicativo Dinamica EGO. Os resultados previstos para 2026 indicam que a cobertura florestal irá se expandir de 14,22% em 2011 para 15,03% no ano de 2026 da área total da Campanha Ocidental, demonstrando que o aumento da cobertura florestal encontra-se em processo de estabilização, concentrando-se suas áreas na parte leste, altitudes elevadas e nas bordas da rede de drenagem. Nos areais, a projeção demonstrou que sua área sofrerá retração de 0,37% em 2011 para 0,33% da área total da região em 2026, e sua concentração estará presente na parte leste, em altitudes elevadas e em torno da drenagem do rio Ibicui.
Menz, William Jefferson. "Stochastic modelling of silicon nanoparticle synthesis". Thesis, University of Cambridge, 2014. https://www.repository.cam.ac.uk/handle/1810/245146.
Pełny tekst źródłaKindl, Mark Richard. "A stochastic approach to path planning in the Weighted-Region Problem". Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26789.
Pełny tekst źródłaRattana, Prapanporn. "Mean-field-like approximations for stochastic processes on weighted and dynamic networks". Thesis, University of Sussex, 2015. http://sro.sussex.ac.uk/id/eprint/56600/.
Pełny tekst źródłaHilton, Cary Allen. "A stochastic approach to solving the 2 _x001B_p1_x001B_s/_x001B_b2_x001B_s dimensional weighted region problem". Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/28563.
Pełny tekst źródłaXu, Zhouyi. "Stochastic Modeling and Simulation of Gene Networks". Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/645.
Pełny tekst źródłaMichel, Simon [Verfasser]. "Stochastic evolution equations in weighted L² spaces with jump noise / Simon Michel. Fakultät für Mathematik". Bielefeld : Universitätsbibliothek Bielefeld, Hochschulschriften, 2012. http://d-nb.info/1022030078/34.
Pełny tekst źródłaSzyszkowicz, B. (Barbara) Carleton University Dissertation Mathematics. "Weak convergence of stochastic processes in weighted metrics and their applications to contiguous changepoint analysis". Ottawa, 1992.
Znajdź pełny tekst źródłaKsiążki na temat "Stochastic weights"
Dohndorf, Iryna. Stochastic graph models with phase type distributed edge weights. Dortmund: Universitätsbibliothek Dortmund, 2017.
Znajdź pełny tekst źródłaLajos, Horváth, red. Weighted approximations in probability and statistics. Chichester: Wiley, 1993.
Znajdź pełny tekst źródłaKindl, Mark Richard. A stochastic approach to path planning in the Weighted-Region Problem. Monterey, Calif: Naval Postgraduate School, 1991.
Znajdź pełny tekst źródłaHilton, Cary Allen. A stochastic approach to solving the 2 ¹/ dimensional weighted region problem. Monterey, Calif: Naval Postgraduate School, 1991.
Znajdź pełny tekst źródłaKindl, Mark R. A stochastic approach to the weighted-region problem: 1. the design of the path annealing algorithm. Monterey, Calif: Naval Postgraduate School, 1991.
Znajdź pełny tekst źródłaP, McCormick William, red. Asymptotic expansions for infinite weighted convolutions of heavy tail distributions and applications. Providence, R.I: American Mathematical Society, 2009.
Znajdź pełny tekst źródłaAlexander, Meskhi, i Persson Lars Erik 1944-, red. Weighted norm inequalities for integral transforms with product kernals. Hauppauge, NY: Nova Science Publishers, 2009.
Znajdź pełny tekst źródłaCoolen, A. C. C., A. Annibale i E. S. Roberts. Random graph ensembles. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0003.
Pełny tekst źródłaL, Taylor Robert. Stochastic Convergence of Weighted Sums of Random Elements in Linear Spaces. Springer London, Limited, 2006.
Znajdź pełny tekst źródłaBack, Kerry E. Alternative Preferences. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0025.
Pełny tekst źródłaCzęści książek na temat "Stochastic weights"
Rigatos, Gerasimos G. "Attractors in Associative Memories with Stochastic Weights". W Advanced Models of Neural Networks, 191–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43764-3_10.
Pełny tekst źródłaRigatos, Gerasimos G. "Spectral Analysis of Neural Models with Stochastic Weights". W Advanced Models of Neural Networks, 207–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43764-3_11.
Pełny tekst źródłaWu, Ying, Colin Fyfe i Pei Ling Lai. "Stochastic Weights Reinforcement Learning for Exploratory Data Analysis". W Lecture Notes in Computer Science, 668–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74690-4_68.
Pełny tekst źródłaBirnbaum, Michael H. "The Paradoxes of Allais, Stochastic Dominance, and Decision Weights". W Decision Science and Technology, 27–52. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5089-1_3.
Pełny tekst źródłaScholz, Roland W. "The ‘Base-Rate Fallacy’ — Heuristics and/or the Modeling of Judgmental Biases by Information Weights". W Cognitive Strategies in Stochastic Thinking, 10–56. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3825-0_2.
Pełny tekst źródłaLee, Haesung, Wilhelm Stannat i Gerald Trutnau. "The Abstract Cauchy Problem in L r-Spaces with Weights". W Analytic Theory of Itô-Stochastic Differential Equations with Non-smooth Coefficients, 9–57. Singapore: Springer Nature Singapore, 2012. http://dx.doi.org/10.1007/978-981-19-3831-3_2.
Pełny tekst źródłaKrylov, Nicolai V. "On Parabolic Pdes and Spdes in Sobolev Spaces W P 2 without and with Weights". W Topics in Stochastic Analysis and Nonparametric Estimation, 151–97. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-75111-5_8.
Pełny tekst źródłaBroadie, Mark, Paul Glasserman i Zachary Ha. "Pricing American Options by Simulation Using a Stochastic Mesh with Optimized Weights". W Nonconvex Optimization and Its Applications, 26–44. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3150-7_2.
Pełny tekst źródłaBiehl, Michael. "The Statistical Physics of Learning Revisited: Typical Learning Curves in Model Scenarios". W Lecture Notes in Computer Science, 128–42. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_10.
Pełny tekst źródłaSala, Dariusz, i Bogusław Bieda. "Role of Stochastic Approach Applied to Life Cycle Inventory (LCI) of Rare Earth Elements (REEs) from Secondary Sources Case Studies". W Towards a Sustainable Future - Life Cycle Management, 107–20. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77127-0_10.
Pełny tekst źródłaStreszczenia konferencji na temat "Stochastic weights"
Fukuda, Yasushi, i Takayuki Kawahara. "Stochastic weights binary neural networks on FPGA". W 2018 7th International Symposium on Next Generation Electronics (ISNE). IEEE, 2018. http://dx.doi.org/10.1109/isne.2018.8394726.
Pełny tekst źródłaFarhat, Nabil H., i Zon Yin Shae. "A Stochastic Optoelectronic Learning Machine". W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/oam.1988.pdp13.
Pełny tekst źródłaRusnak, Rastislav, i Rudolf Jaksa. "Stochastic weights and neurons selection in neural networks for weather prediction". W 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2016. http://dx.doi.org/10.1109/sami.2016.7423034.
Pełny tekst źródłaChunfu Jia. "Stochastic single machine scheduling with earliness and tardiness penalties and proportional weights". W Proceedings of 2002 American Control Conference. IEEE, 2002. http://dx.doi.org/10.1109/acc.2002.1025351.
Pełny tekst źródłaTabacek, Jaroslav, i Vladimir Havlena. "Desensitized Extended Kalman Filter with Stochastic Approach to Sensitivity Reduction and Adaptive Weights". W 2022 25th International Conference on Information Fusion (FUSION). IEEE, 2022. http://dx.doi.org/10.23919/fusion49751.2022.9841381.
Pełny tekst źródłaHall, T. J., W. Peiffer, M. Hands, H. Thienpont, W. A. Crossland, J. S. Shawe-Taylor i M. van Daalen. "Considerations of the Optical and Opto-electronic Hardware Requirements for Implementation of Stochastic Bit-stream Neural Nets". W Optical Computing. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/optcomp.1995.otue18.
Pełny tekst źródłaCaniou, Yves, Eddy Caron, Aurelie Kong Win Chang i Yves Robert. "Budget-Aware Scheduling Algorithms for Scientific Workflows with Stochastic Task Weights on Heterogeneous IaaS Cloud Platforms". W 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2018. http://dx.doi.org/10.1109/ipdpsw.2018.00014.
Pełny tekst źródłaZuo, Lei, i Samir A. Nayfeh. "Adaptive Least-Mean Square Feed-Forward Control With Actuator Saturation by Direct Minimization". W ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85494.
Pełny tekst źródłaNoura, A. A., i M. Nozohour. "Extension of ranking method based on effectiveness of units in society by common weights approach in stochastic DEA". W INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND STATISTICS 2013 (ICMSS2013): Proceedings of the International Conference on Mathematical Sciences and Statistics 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4823896.
Pełny tekst źródłaSambaturu, Prathyush, Marco Minutoli, Mahantesh Halappanavar, Ananth Kalyanaraman i Anil Vullikanti. "Scalable and Memory-Efficient Algorithms for Controlling Networked Epidemic Processes Using Multiplicative Weights Update Method". W Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/717.
Pełny tekst źródłaRaporty organizacyjne na temat "Stochastic weights"
Snyder, Victor A., Dani Or, Amos Hadas i S. Assouline. Characterization of Post-Tillage Soil Fragmentation and Rejoining Affecting Soil Pore Space Evolution and Transport Properties. United States Department of Agriculture, kwiecień 2002. http://dx.doi.org/10.32747/2002.7580670.bard.
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