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Journal articles on the topic 'Stochastické metody'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Shymanska, Alla V., and Vitali A. Babakov. "Fast Monte Carlo Method in Stochastic Modelling of Charged Particle Multiplication." International Journal of Applied Physics and Mathematics 5, no. 3 (2015): 218–26. http://dx.doi.org/10.17706/ijapm.2015.5.3.218-226.

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12

Marianto, Fredy Yoseph, Tarno Tarno, and Di Asih I. Maruddani. "PERBANDINGAN METODE NAÏVE BAYES DAN BAYESIAN REGULARIZATION NEURAL NETWORK (BRNN) UNTUK KLASIFIKASI SINYAL PALSU PADA INDIKATOR STOCHASTIC OSCILLATOR (Studi Kasus: Saham PT Bank Rakyat Indonesia (Persero) Tbk Periode Januari 2017 – Agustus 2019)." Jurnal Gaussian 9, no. 1 (2020): 16–25. http://dx.doi.org/10.14710/j.gauss.v9i1.27520.

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Keputusan untuk membeli atau menjual saham merupakan kunci utama untuk memperoleh keuntungan dalam trading dan investasi. Salah satu indikator yang dapat digunakan dalam menentukan momentum untuk membeli atau menjual saham adalah Stochastic Oscillator. Sebagai indikator yang sensitif terhadap pergerakan harga saham, Stochastic Oscillator sering mengeluarkan sinyal palsu yang mengakibatkan kerugian dalam transaksi. Terdapat 9 atribut yang diduga dapat mengidentifikasi apakah suatu sinyal yang keluar dari indikator Stochastic Oscillator merupakan sinyal palsu atau tidak. Tujuan dari penelitian i

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13

Cho. "Analysis of the Applicability of Parameter Estimation Methods for a Stochastic Rainfall Model." Journal of the Korean Society of Civil Engineers 34, no. 4 (2014): 1105. http://dx.doi.org/10.12652/ksce.2014.34.4.1105.

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14

Hasbiyati, Ihda, and Hasriati Hasriati. "Beberapa Metode pada Masalah Pemrograman Stokastik." Jurnal Matematika "MANTIK" 3, no. 2 (2017): 83–86. http://dx.doi.org/10.15642/mantik.2017.3.2.83-86.

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Stochastic programming problem is mathematical problem (linear, integer, mixed integer, and nonlinier) with stochastic element lies data. To get reasonable solution and optimal with its stochastic data is needed several method. Applicable method in trouble stochastic programming are L-Shape decomposition and lagrange decomposition. Each method can determine optimal solution to troubleshoots stochastic programming

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15

Klimavičienė, Aušra. "STOCHASTIC OPTIMIZATION OF HEURISTIC METHOD RULE TO DETERMINE ASSET ALLOCATION TO RETIREMENT PORTFOLIO / STOCHASTINIS EURISTINIO METODO TAISYKLĖS PENSIJOS PORTFELIO SUDĖČIAI NUSTATYTI OPTIMIZAVIMAS." Business: Theory and Practice 12, no. 1 (2011): 92–98. http://dx.doi.org/10.3846/btp.2011.10.

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The article examines the problem of determining asset allocation to sustainable retirement portfolio. The article attempts to apply heuristic method – 100 minus age in stocks rule – to determine asset allocation to sustainable retirement portfolio. Using dynamic stochastic simulation and stochastic optimization techniques the optimization of heuristic method rule is presented and the optimal alternative to „100“ is found. Seeking to reflect the stochastic nature of stock and bond returns and the human lifespan, the dynamic stochastic simulation models incorporate both the stochastic returns an

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16

Moradi Ghahderijani, M., S. M. Barakati, and Ahmad Jamshidi. "Application of Stochastic Simulation Method in Reliability Assessment of a PV-Wind-Diesel-SOFC Hybrid Microgrid." International Journal of Engineering and Technology 4, no. 5 (2012): 586–89. http://dx.doi.org/10.7763/ijet.2012.v4.438.

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17

Aubin, Jean-Pierre, and Giuseppe Da Prato. "Stochastic Lyapunov method." Nonlinear Differential Equations and Applications NoDEA 2, no. 4 (1995): 511–25. http://dx.doi.org/10.1007/bf01210621.

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18

Ohzeki, Masayuki. "Stochastic gradient method with accelerated stochastic dynamics." Journal of Physics: Conference Series 699 (March 2016): 012019. http://dx.doi.org/10.1088/1742-6596/699/1/012019.

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19

Morita, Hiroshi, and Hiroaki Ishii. "A stochastic improvement method for stochastic programming." Computational Statistics & Data Analysis 14, no. 4 (1992): 477–87. http://dx.doi.org/10.1016/0167-9473(92)90063-l.

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20

Richtárik, Mojmír Mutný and Peter. "Parallel Stochastic Newton Method." Journal of Computational Mathematics 36, no. 3 (2018): 404–25. http://dx.doi.org/10.4208/jcm.1708-m2017-0113.

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21

Enokizono, M., and M. Aoki. "Stochastic Boundary Element Method." IEEE Translation Journal on Magnetics in Japan 2, no. 12 (1987): 1115–16. http://dx.doi.org/10.1109/tjmj.1987.4549708.

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22

Luo, Zhijian, and Yuntao Qian. "Stochastic sub-sampled Newton method with variance reduction." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 06 (2019): 1950041. http://dx.doi.org/10.1142/s0219691319500413.

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Stochastic optimization on large-scale machine learning problems has been developed dramatically since stochastic gradient methods with variance reduction technique were introduced. Several stochastic second-order methods, which approximate curvature information by the Hessian in stochastic setting, have been proposed for improvements. In this paper, we introduce a Stochastic Sub-Sampled Newton method with Variance Reduction (S2NMVR), which incorporates the sub-sampled Newton method and stochastic variance-reduced gradient. For many machine learning problems, the linear time Hessian-vector pro

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23

Chertock, A., A. Kurganov, M. Lukáčová-Medvid’ová, P. Spichtinger, and B. Wiebe. "Stochastic Galerkin method for cloud simulation." Mathematics of Climate and Weather Forecasting 5, no. 1 (2019): 65–106. http://dx.doi.org/10.1515/mcwf-2019-0005.

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AbstractWe develop a stochastic Galerkin method for a coupled Navier-Stokes-cloud system that models dynamics of warm clouds. Our goal is to explicitly describe the evolution of uncertainties that arise due to unknown input data, such as model parameters and initial or boundary conditions. The developed stochastic Galerkin method combines the space-time approximation obtained by a suitable finite volume method with a spectral-type approximation based on the generalized polynomial chaos expansion in the stochastic space. The resulting numerical scheme yields a second-order accurate approximatio

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24

Kadry, Seifedine. "On the Theory of Stochastic Transformation Method." Advanced Materials Research 702 (May 2013): 304–9. http://dx.doi.org/10.4028/www.scientific.net/amr.702.304.

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The Stochastic Finite Element Method (SFEM) represents a new approach to solve mechanical systems with stochastic characteristics. The SFEM is based on the deterministic Finite Element Method in which some variables related to the structural state (variables involved in the stiffness matrix) and to the applied actions (involved in the load vector) are uncertain. In other words, the SFEM tries to look for the stochastic properties of the mechanical response. The solution of a stochastic mechanical system is completely defined through the evaluation of the probability density function of the res

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25

Sengul, Suleyman, Zafer Bekiryazici, and Mehmet Merdan. "Wong-Zakai method for stochastic differential equations in engineering." Thermal Science 25, Spec. issue 1 (2021): 131–42. http://dx.doi.org/10.2298/tsci200528014s.

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In this paper, Wong-Zakai approximation methods are presented for some stochastic differential equations in engineering sciences. Wong-Zakai approximate solutions of the equations are analyzed and the numerical results are compared with results from popular approximation schemes for stochastic differential equations such as Euler-Maruyama and Milstein methods. Several differential equations from engineering problems containing stochastic noise are investigated as numerical examples. Results show that Wong-Zakai method is a reliable tool for studying stochastic differential equations and can be

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26

Oakley, David R., and Robert H. Sues. "Stochastic Optimization Using the Stochastic Preconditioned Conjugate Gradient Method." AIAA Journal 34, no. 9 (1996): 1969–71. http://dx.doi.org/10.2514/3.60034.

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27

Chau, Ki Wai, and Cornelis W. Oosterlee. "Stochastic grid bundling method for backward stochastic differential equations." International Journal of Computer Mathematics 96, no. 11 (2019): 2272–301. http://dx.doi.org/10.1080/00207160.2019.1658868.

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28

Iwai, Toshiya. "Study of stochastic resonance by method of stochastic energetics." Physica A: Statistical Mechanics and its Applications 300, no. 3-4 (2001): 350–58. http://dx.doi.org/10.1016/s0378-4371(01)00349-1.

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29

Zhang, Haisen. "Stochastic Theta Method for a Reflected Stochastic Differential Equation." Numerical Functional Analysis and Optimization 35, no. 6 (2014): 752–76. http://dx.doi.org/10.1080/01630563.2013.837068.

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30

Lee, Hyung-Chun, and Yun Nam. "SPARSE GRID STOCHASTIC COLLOCATION METHOD FOR STOCHASTIC BURGERS EQUATION." Journal of the Korean Mathematical Society 54, no. 1 (2017): 193–213. http://dx.doi.org/10.4134/jkms.j150673.

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31

Promsinchai, Porntip, Ali Farajzadeh, and Narin Petrot. "Stochastic Heavy-Ball Method for Constrained Stochastic Optimization Problems." Acta Mathematica Vietnamica 45, no. 2 (2020): 501–14. http://dx.doi.org/10.1007/s40306-019-00357-y.

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32

Yang, LuFeng, A. Y. T. Leung, Liubin Yan, and C. W. Y. Wong. "Stochastic spline Ritz method based on stochastic variational principle." Engineering Structures 27, no. 3 (2005): 455–62. http://dx.doi.org/10.1016/j.engstruct.2004.11.006.

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33

Ermol'ev, Yu M., and V. I. Norkin. "Stochastic generalized gradient method for nonconvex nonsmooth stochastic optimization." Cybernetics and Systems Analysis 34, no. 2 (1998): 196–215. http://dx.doi.org/10.1007/bf02742069.

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34

Ladde, Gangarm S., and Youngsoo Seol. "Lévy-type nonlinear stochastic dynamic model, method and analysis." Stochastics and Dynamics 19, no. 05 (2019): 1950033. http://dx.doi.org/10.1142/s0219493719500333.

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In this work, we consider a prototype stochastic dynamic model for dynamic processes in biological, chemical, economic, financial, medical, military, physical and technological sciences. The dynamic model is described by Lévy-type nonlinear stochastic differential equation. The model validation is established by the usage of Lyapunov-like function. The basic innovative idea is to transform a nonlinear Lévy-type nonlinear stochastic differential into a simpler stochastic differential equation that is easily tested for the existence and uniqueness theorem. Using the nature of Lyapunov-like funct

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35

Zhao, Yu, Xi Zhang, Zhongshun Shi, and Lei He. "Grain Price Forecasting Using a Hybrid Stochastic Method." Asia-Pacific Journal of Operational Research 34, no. 05 (2017): 1750020. http://dx.doi.org/10.1142/s0217595917500208.

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Grain price forecasting is significant for all market participants in managing risks and planning operations. However, precise forecasting of price series is difficult because of the inherent stochastic behavior of grain price. In this paper, a novel hybrid stochastic method for grain price forecasting is introduced. The proposed method combines decomposed stochastic time series processes with artificial neural networks. The initial parameters of the hybrid stochastic model are optimized by a random search using a genetic algorithm. The proposed method is finally validated in China’s national

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36

Kohatsu-Higa, Arturo, and Gô Yûki. "Stochastic formulations of the parametrix method." ESAIM: Probability and Statistics 22 (2018): 178–209. http://dx.doi.org/10.1051/ps/2018013.

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In this manuscript, we consider stochastic expressions of the parametrix method for solutions of d-dimensional stochastic differential equations (SDEs) with drift coefficients which belong to Lp(Rd), p > d. We prove the existence and Hölder continuity of probability density functions for distributions of solutions at fixed points and obtain an explicit expansion via (stochastic) parametrix methods. We also obtain Gaussian type upper and lower bounds for these probability density functions.

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37

Zhang, Zhiwen, Xin Hu, Thomas Y. Hou, Guang Lin, and Mike Yan. "An Adaptive ANOVA-Based Data-Driven Stochastic Method for Elliptic PDEs with Random Coefficient." Communications in Computational Physics 16, no. 2 (2014): 571–98. http://dx.doi.org/10.4208/cicp.270913.020414a.

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AbstractIn this paper, we present an adaptive, analysis of variance (ANOVA)-based data-driven stochastic method (ANOVA-DSM) to study the stochastic partial differential equations (SPDEs) in the multi-query setting. Our new method integrates the advantages of both the adaptive ANOVA decomposition technique and the data-driven stochastic method. To handle high-dimensional stochastic problems, we investigate the use of adaptive ANOVA decomposition in the stochastic space as an effective dimension-reduction technique. To improve the slow convergence of the generalized polynomial chaos (gPC) method

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38

Umar, Rusydi, Imam Riadi, and Purwono Purwono. "Klasifikasi Kinerja Programmer pada Aktivitas Media Sosial dengan Metode Stochastic Gradient Descent." JOINTECS (Journal of Information Technology and Computer Science) 5, no. 2 (2020): 55. http://dx.doi.org/10.31328/jointecs.v5i2.1324.

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Kegagalan perusahaan pemula berbasis teknologi (startup) di Indonesia diakibatkan oleh kurang solidnya kinerja tim serta banyaknya kesalahan dalam proses rekrutmen programmer. Kemajuan pesat dalam budaya bermedia sosial dapat dimanfaatkan sebagai salah satu metode untuk memperoleh kandidat programmer terbaik dalam startup. Metode perekrutan yang digunakan dapat berupa melakukan proses klasifikasi konten media sosial kandidat programmer. Klasifikasi tersebut diharapkan dapat menemukan pola kinerja kandidat programmer, dengan hasil baik atau buruk. Metode klasifikasi yang dapat digunakan salah s

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39

Jiang, Zhongming, and Jie Li. "A New Reliability Method Combining Kriging and Probability Density Evolution Method." International Journal of Structural Stability and Dynamics 17, no. 10 (2017): 1750113. http://dx.doi.org/10.1142/s0219455417501139.

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Stochastic dynamic analysis of structures with random parameters continues to be an open question in the field of civil engineering. As a newly developed method, the probability density evolution method (PDEM) can provide the probability density function (PDF) of the dynamic responses of highly nonlinear structures. In this paper, a new method based on PDEM and the kriging surrogate model, named the K-PDEM, is proposed to study the stochastic response of a structure. Being an exact interpolation method, the Gaussian process regression or the so-called kriging method is capable of producing hig

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40

Chen, Weidong, Yaqin Shi, Jingxin Ma, Chunlong Xu, Shengzhuo Lu, and Xing Xu. "Stochastic Material Point Method for Analysis in Non-Linear Dynamics of Metals." Metals 9, no. 1 (2019): 107. http://dx.doi.org/10.3390/met9010107.

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A stochastic material point method is proposed for stochastic analysis in non-linear dynamics of metals with varying random material properties. The basic random variables are parameters of equation of state and those of constitutive equation. In conjunction with the material point method, the Taylor series expansion is employed to predict first- and second-moment characteristics of structural response. Unlike the traditional grid methods, the stochastic material point method does not require structured mesh; instead, only a scattered cluster of nodes is required in the computational domain. I

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41

Hu, Zhi Gang, Yong Lin Zhang, Jian Ping Ye, Shao Yun Song, and Li Ping Chen. "Numerical Modeling and Simulation of Random Road Surface Using IFFT Method." Advanced Materials Research 199-200 (February 2011): 999–1004. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.999.

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Based on the power spectral density (PSD) function of stochastic irregularities of the standard grade road and by means of inverse fast Fouerier transform (IFFT) based on discretized PSD sampling, an equivalent sample of stochastic road surface model in time domain was built. A one-dimensional model of stochastic road was developed into a 2D model of stochastic road surface. Through computer simulation practice based on the MATlab, a 2D sample of stochastic road surface in time domain was regenerated. Furthermore, given the sample data, the PSD was estimated and then compared with the theoreti

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42

Kuswandanu, Eruit. "Analisis Portofolio Optimum Saham Syariah Menggunakan Stochastic Dominance." Jurnal Fourier 4, no. 1 (2015): 17. http://dx.doi.org/10.14421/fourier.2015.41.17-29.

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Hal terpenting yang diperhatikan oleh seorang investor adalah bagaimana investasi dapat menghasilkan return optimal pada tingkat resiko yang minimal, sehingga untuk mengetahui hal tersebut investor dapat melakukan diversifikasi. Diversifikasi dapat diwujudkan dengan cara mengkombinasikan berbagai pilihan saham dalam investasi (membentuk portofolio saham optimal). Salah satu teknik analisa portofolio optimal adalah menggunakan metode stochastic dominance, dimana metode ini tidak mensyaratkan distribusi dari return investasi harus bersifat normal. Penelitian ini menggunakan studi kasus pada saha

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43

Daly, Fraser, Claude Lefèvre, and Sergey Utev. "Stein's Method and Stochastic Orderings." Advances in Applied Probability 44, no. 02 (2012): 343–72. http://dx.doi.org/10.1017/s0001867800005632.

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A stochastic ordering approach is applied with Stein's method for approximation by the equilibrium distribution of a birth-death process. The usual stochastic order and the more general s-convex orders are discussed. Attention is focused on Poisson and translated Poisson approximations of a sum of dependent Bernoulli random variables, for example, k-runs in independent and identically distributed Bernoulli trials. Other applications include approximation by polynomial birth-death distributions.

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44

Yin, Guosheng, Yanyuan Ma, Faming Liang, and Ying Yuan. "Stochastic Generalized Method of Moments." Journal of Computational and Graphical Statistics 20, no. 3 (2011): 714–27. http://dx.doi.org/10.1198/jcgs.2011.09210.

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45

Xu, Jin, Rongji Mu, and Cui Xiong. "A Bayesian stochastic approximation method." Journal of Statistical Planning and Inference 211 (March 2021): 391–401. http://dx.doi.org/10.1016/j.jspi.2020.07.006.

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46

Daly, Fraser, Claude Lefèvre, and Sergey Utev. "Stein's Method and Stochastic Orderings." Advances in Applied Probability 44, no. 2 (2012): 343–72. http://dx.doi.org/10.1239/aap/1339878715.

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A stochastic ordering approach is applied with Stein's method for approximation by the equilibrium distribution of a birth-death process. The usual stochastic order and the more general s-convex orders are discussed. Attention is focused on Poisson and translated Poisson approximations of a sum of dependent Bernoulli random variables, for example, k-runs in independent and identically distributed Bernoulli trials. Other applications include approximation by polynomial birth-death distributions.

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47

Liddle, Roger F., and John F. Monahan. "A stationary stochastic approximation method." Journal of Econometrics 38, no. 1-2 (1988): 91–102. http://dx.doi.org/10.1016/0304-4076(88)90028-0.

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48

Arnaudon, Alexis, Nader Ganaba, and Darryl D. Holm. "The stochastic energy-Casimir method." Comptes Rendus Mécanique 346, no. 4 (2018): 279–90. http://dx.doi.org/10.1016/j.crme.2018.01.003.

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49

Namachchivaya, N. Sri, and Y. K. Lin. "Method of stochastic normal forms." International Journal of Non-Linear Mechanics 26, no. 6 (1991): 931–43. http://dx.doi.org/10.1016/0020-7462(91)90042-r.

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50

Park, Young Jun, Han Seong Gwak, Byung Soo Kim, and Dong-Eun Lee. "Stochastic dozer productivity estimation method." KSCE Journal of Civil Engineering 21, no. 5 (2016): 1573–80. http://dx.doi.org/10.1007/s12205-016-1596-9.

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