Relevant bibliographies by topics / Stochastické metody

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Stochastické metody'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Stochastické metody"

1

Borovička, Adam. "Analysis of the Capital Market Via Stochastic Dominance and Multi-Criteria Interactive Method." Acta Oeconomica Pragensia 21, no. 1 (2013): 26–45. http://dx.doi.org/10.18267/j.aop.391.

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Hu, Peng, та Chengming Huang. "The StochasticΘ-Method for Nonlinear Stochastic Volterra Integro-Differential Equations". Abstract and Applied Analysis 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/583930.

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The stochasticΘ-method is extended to solve nonlinear stochastic Volterra integro-differential equations. The mean-square convergence and asymptotic stability of the method are studied. First, we prove that the stochasticΘ-method is convergent of order1/2in mean-square sense for such equations. Then, a sufficient condition for mean-square exponential stability of the true solution is given. Under this condition, it is shown that the stochasticΘ-method is mean-square asymptotically stable for every stepsize if1/2≤θ≤1and when0≤θ<1/2, the stochasticΘ-method is mean-square asymptotically stable
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Zeghdane, R. "Новый численный метод решения нелинейных стохастических интегральных уравнений". Владикавказский математический журнал, № 4() (22 грудня 2020): 68–86. http://dx.doi.org/10.46698/n8076-2608-1378-r.

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The purpose of this paper is to propose the Chebyshev cardinal functions for solving Volterra stochastic integral equations. The method is based on expanding the required approximate solution as the element of Chebyshev cardinal functions. Though the way, a new operational matrix of integration is derived for the mentioned basis functions. More precisely, the unknown solution is expanded in terms of the Chebyshev cardinal functions including undetermined coefficients. By substituting the mentioned expansion in the original problem, the operational matrix reducing the stochastic integral equati
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Suzuki, Taiji. "STOCHASTIC ALTERNATING DIRECTION METHOD OF MULTIPLIERS FOR STRUCTURED REGULARIZATION." Journal of the Japanese Society of Computational Statistics 28, no. 1 (2015): 105–24. http://dx.doi.org/10.5183/jjscs.1502004_218.

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Novoselova-, Marina, Svetlana Ivanova, Valeriy Pavskiy, and Mariya Poplavskaya. "Studying the Biokinetics of Pigmented Yeast by Stochastic Methods." Foods and Raw Materials 2, no. 1 (2014): 17–21. http://dx.doi.org/10.12737/4117.

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Ivanova, Svetlana. "Studing the Foaming of Protein Solutions by Stochastic Methods." Foods and Raw Materials 2, no. 2 (2014): 140–46. http://dx.doi.org/10.12737/5472.

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Dai, Zhaohui, and Shanshan Jiang. "High Order Compact Splitting Method for Stochastic Nonlinear Schrödinger Equation." International Journal of Modeling and Optimization 4, no. 4 (2014): 310–14. http://dx.doi.org/10.7763/ijmo.2014.v4.391.

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Nishizawa, Kazutomo, and Iwaro Takahashi. "ESTIMATION METHODS BY STOCHASTIC MODEL IN BINARY AND TERNARY AHP." Journal of the Operations Research Society of Japan 50, no. 2 (2007): 101–22. http://dx.doi.org/10.15807/jorsj.50.101.

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Herdiana, Ratna. "NUMERICAL SIMULATION OF STOCHASTIC DIFFERENTIAL EQUATIONS USING IMPLICIT MILSTEIN METHOD." Journal of Fundamental Mathematics and Applications (JFMA) 3, no. 1 (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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Klyatskin, Valerii I. "Modern methods for the statistical description of dynamic stochastic systems." Uspekhi Fizicheskih Nauk 179, no. 5 (2009): 547. http://dx.doi.org/10.3367/ufnr.0179.200905j.0547.

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Dissertations / Theses on the topic "Stochastické metody"

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Vacek, Vladislav. "Stochastické metody v řízení portfolia." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-73894.

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From the beginning of 20th century many studies proved randomness in price evolution of investment instruments. Therefore models respecting this randomness must be used in portfolio management. This thesis' aim is to provide basic theory regarding some of the stochastic methods and show their practical use in real situations.
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Zemenová, Hana. "Stochastické metody v řízení projektů." Master's thesis, Vysoká škola ekonomická v Praze, 2007. http://www.nusl.cz/ntk/nusl-2349.

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Každý projekt je ze své povahy spojen s jistou dávkou rizika a nejistoty, kterou je nutné zohlednit při volbě adekvátních metod pro jeho řízení. Cílem práce je tyto metody klasifikovat, porovnat a aplikovat na případové studii z podnikové praxe. Podrobněji jsou přitom rozebrány právě ty metody, které byly vhodné pro zkoumaný projekt z případové studie: jedná se o metodu CPM/PERT, simulaci Monte Carlo a analýzu projektu prostřednictvím bayesovských sítí.
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Kubelka, Lukáš. "Metody stochastického programováni pro investiční rozhodování." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-233045.

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This thesis deals with methods of stochastic programming and their application in financial investment. Theoretical part is devoted to basic terms of mathematical optimization, stochastic programming and decision making under uncertainty. Furter, there are introduced basic principles of modern portfolio theory, substantial part is devoted to risk measurement techniques in the context of investment, mostly to the methods Value at Risk and Expected shortfall. Practical part aims to creation of optimization models with an emphasis to minimize investment risk. Created models deal with real data an
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Janečka, Adam. "Stochastické rovnice a numerické řešení modelu oceňování opcí." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-195450.

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In the present work, we study the topic of stochastic differential equations, their numerical solution and solution of models for pricing of options which follow from stochastic differential equations using the Itô calculus. We present several numerical methods for solving stochastic differential equations. These methods are then implemented in MATLAB and we investigate their properties, especially their convergence characteristics. Furthermore, we formulate two models for pricing of European call options. We solve these models using a variant of the spectral collocation method, again in MATLA
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Kůdela, Jakub. "Advanced Decomposition Methods in Stochastic Convex Optimization." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-403864.

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Při práci s úlohami stochastického programování se často setkáváme s optimalizačními problémy, které jsou příliš rozsáhlé na to, aby byly zpracovány pomocí rutinních metod matematického programování. Nicméně, v některých případech mají tyto problémy vhodnou strukturu, umožňující použití specializovaných dekompozičních metod, které lze použít při řešení rozsáhlých optimalizačních problémů. Tato práce se zabývá dvěma třídami úloh stochastického programování, které mají speciální strukturu, a to dvoustupňovými stochastickými úlohami a úlohami s pravděpodobnostním omezením, a pokročilými dekompozi
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Červenka, Daniel. "Aplikace metod optimalizace zásob v dodavatelských řetězcích." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-150222.

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As in the stock of trading business is allocated a large part of the capital resources, it is necessary to determine the manner of their control. For this purpose a number of models were developed. Before application to the specific case, these models must be properly adjusted to ensure conformity with reality. The aim of this thesis is to optimize the inventory management of electronic commerce. The stochastic model with loss from unfulfilled orders was chosen as default. First, the necessary adjustments were made to the model and defined input parameters. After filling model with real data,
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Kobulnická, Ivana. "Stochastické metódy v riadení portfólia." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-359285.

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This master thesis aims to describe and apply in practice solutions of basic tasks in portfolio management- portfolio optimization, portfolio modelling and risk management. As value of financial assets in future is a random variable, it is necessary to use mathematic tools resulting from probability theory and statistics. Basic terms from this area are for example stochastic Wiener process or geometric Brownian motion, which are described in first part of this thesis. Next parts of thesis describe the Markowitz model or method Value at Risk. In the last part of thesis is application of calcula
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Kozel, Tomáš. "Stochastické řízení zásobní funkce nádrže s pomocí metod umělé inteligence." Doctoral thesis, Vysoké učení technické v Brně. Fakulta stavební, 2018. http://www.nusl.cz/ntk/nusl-390282.

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The main advantage of stochastic forecasting is fan of possible value, which deterministic method of forecasting could not give us. Future development of random process is described better by stochastic then deterministic forecasting. We can categorize discharge in measurement profile as random process. Stochastic management is worked with dispersion of controlling discharge value. In thesis is described construction and evaluation of adaptive stochastic model base on fuzzy logic, neural networks and evolution algorithm, which are used stochastic forecast from forecasting models described in t
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Hrbek, Filip. "Metody předvídání volatility." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264689.

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In this masterthesis I have rewied basic approaches to volatility estimating. These approaches are based on classical and Bayesian statistics. I have applied the volatility models for the purpose of volatility forecasting of a different foreign exchange (EURUSD, GBPUSD and CZKEUR) in the different period (from a second period to a day period). I formulate the models EWMA, GARCH, EGARCH, IGARCH, GJRGARCH, jump diffuison with constant volatility and jump diffusion model with stochastic volatility. I also proposed an MCMC algorithm in order to estimate the Bayesian models. All the models we estim
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Čajánek, Michal. "Modely stochastického programování v inženýrském návrhu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2009. http://www.nusl.cz/ntk/nusl-228544.

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Two-stage stochastic programming problem with PDE constraint, specially elliptic equation is formulated. The computational scheme is proposed, whereas the emphasis is put on approximation techniques. We introduce method of approximation of random variables of stochastic problem and utilize suitable numerical methods, finite difference method first, then finite element method. There is also formulated a mathematical programming problem describing a membrane deflection with random load. It is followed by determination of the acceptableness of using stochastic optimization rather than determinist
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Books on the topic "Stochastické metody"

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Kurt, Marti. Stochastic optimization methods. Springer, 2004.

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service), SpringerLink (Online, ed. Stochastic Optimization Methods. Springer-Verlag Berlin Heidelberg, 2008.

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Toral, Raúl, and Pere Colet, eds. Stochastic Numerical Methods. Wiley-VCH Verlag GmbH & Co. KGaA, 2014. http://dx.doi.org/10.1002/9783527683147.

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Marti, Kurt. Stochastic Optimization Methods. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79458-5.

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Marti, Kurt. Stochastic Optimization Methods. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46214-0.

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Stochastic optimization methods. Springer, 2005.

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Numerical methods for stochastic computations: A spectral method approach. Princeton University Press, 2010.

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Szopa, Janusz. Metody badania i wrażliwość dynamicznych układów stochastycznych. Dział Wydawnictw Politechniki Śląskiej, 1985.

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Doltsinis, I. St. Stochastic methods in engineering. WIT, 2012.

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Papadopoulos, Vissarion, and Dimitris G. Giovanis. Stochastic Finite Element Methods. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-64528-5.

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Book chapters on the topic "Stochastické metody"

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Walls, D. F., and G. J. Milburn. "Stochastic Methods." In Quantum Optics. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-79504-6_6.

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Boender, C. Guus E., and H. Edwin Romeijn. "Stochastic Methods." In Nonconvex Optimization and Its Applications. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2025-2_15.

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Heermann, Dieter W. "Stochastic Methods." In Computer Simulation Methods in Theoretical Physics. Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-96971-3_4.

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Lantuéjoul, Christian. "Stochastic Methods." In Mathematical Morphology. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118600788.ch5.

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Heermann, Dieter W. "Stochastic Methods." In Computer Simulation Methods in Theoretical Physics. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75448-7_4.

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Rappaz, Michel, Michel Bellet, and Michel Deville. "Stochastic Methods." In Numerical Modeling in Materials Science and Engineering. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11821-0_9.

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Prékopa, András. "Special Problems and Methods." In Stochastic Programming. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-017-3087-7_3.

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Rao, M. M. "Sampling Methods for Processes." In Stochastic Processes. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-6596-0_6.

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Cavazzuti, Marco. "Stochastic Optimization." In Optimization Methods. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31187-1_5.

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Marti, Kurt. "Stochastic Optimization Methods." In Stochastic Optimization Methods. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46214-0_1.

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Conference papers on the topic "Stochastické metody"

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Kasprzak, W., and A. Weron. "Stochastic Methods in Experimental Sciences." In Conference on Stochastic Methods in Experimental Sciences, COSMEX'89. WORLD SCIENTIFIC, 1990. http://dx.doi.org/10.1142/9789814540735.

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Bryans, Jeremy, and John Derrick. "Stochastic Specification and Verification." In 3rd Irish Workshop on Formal Methods. BCS Learning & Development, 1999. http://dx.doi.org/10.14236/ewic/iwfm1999.3.

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Alibrandi, Umberto, and Armen Der Kiureghian. "Model Correction Factor Method for Non-linear Stochastic Dynamic Analysis." In 6th International Conference on Computational Stochastic Mechanics. Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p001.

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Li, Jie, Jianbing Chen, and Weiling Sun. "Advances of Probability Density Evolution Method for Nonlinear Stochastic Systems." In 6th International Conference on Computational Stochastic Mechanics. Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p042.

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Le, T. V., N. Kabachi, and M. Lamure. "A clustering method associated pretopological concepts and k-means algorithm." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0062.

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Kougioumtzoglou, Ioannis A., and Pol D. Spanos. "A Wiener Path Integral Method for Non-Stationary Response Determination of Nonlinear Oscillators Under Random Loading." In 6th International Conference on Computational Stochastic Mechanics. Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p041.

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Polovinkin, A. V. "The modified optimal path method and non-adiabatic II-order transitions in noisy perturbed dynamical systems." In Stochastic and chaotic dynamics in the lakes. AIP, 2000. http://dx.doi.org/10.1063/1.1302366.

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Er, Guo-Kang, and Xiu-Xiu Guo. "Finite Element Method for the Probabilistic Solution of Nonlinear Stochastic Dynamic Oscillators with Poisson White Noise Excitation." In 6th International Conference on Computational Stochastic Mechanics. Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p025.

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X., Frank Xu. "On Quasi-Weak and Weak Formulations of Displacement-based and Green-Function-based Stochastic Finite Element Methods." In 6th International Conference on Computational Stochastic Mechanics. Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p061.

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Saidane, Mohamed, and Christian Lavergne. "Learning and Inference in Switching Conditionally Heteroscedastic Factor Models Using Variational Methods." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0038.

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Reports on the topic "Stochastické metody"

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X. Frank Xu. Numerical Stochastic Homogenization Method and Multiscale Stochastic Finite Element Method - A Paradigm for Multiscale Computation of Stochastic PDEs. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/1036255.

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Dupuis, Paul, and Harold J. Kushner. Numerical Methods in Stochastic Control. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada313649.

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Dupuis, Paul, and Harold Kushner. Computational Methods for Stochastic Networks. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada567599.

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Sharp, D. H., S. Habib, and M. B. Mineev. Numerical Methods for Stochastic Partial Differential Equations. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/759177.

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Webster, Clayton G., Guannan Zhang, and Max D. Gunzburger. An adaptive wavelet stochastic collocation method for irregular solutions of stochastic partial differential equations. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1081925.

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Fitzpatrick, Ben G. Idempotent Methods for Continuous Time Nonlinear Stochastic Control. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada580394.

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Caflisch, Russel E. Nonlinear and Stochastic Numerical Methods and Their Applications. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada284863.

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Prokopyev, Oleg A. New Theory and Methods in Stochastic Mixed Integer Programming. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada610045.

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Powell, Warren B. Stochastic Set Partitioning Methods for Operational Planning of Aircraft. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada387070.

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Willcox, K., D. Allaire, J. Deyst, C. He, and G. Sondecker. Stochastic Process Decision Methods for Complex-Cyber-Physical Systems. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada552217.

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