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Journal articles on the topic 'Stochastic process'

To see the other types of publications on this topic, follow the link: Stochastic process.

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Engelbert, H. J., and V. P. Kurenok. "On Multidimensional SDEs Without Drift and with A Time-Dependent Diffusion Matrix." Georgian Mathematical Journal 7, no. 4 (December 2000): 643–64. http://dx.doi.org/10.1515/gmj.2000.643.

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Abstract We study multidimensional stochastic equations where x o is an arbitrary initial state, W is a d-dimensional Wiener process and is a measurable diffusion coefficient. We give sufficient conditions for the existence of weak solutions. Our main result generalizes some results obtained by A. Rozkosz and L. Słomiński [Stochastics Stochasties Rep. 42: 199–208, 1993] and T. Senf [Stochastics Stochastics Rep. 43: 199–220, 1993] for the existence of weak solutions of one-dimensional stochastic equations and also some results by A. Rozkosz and L. Słomiński [Stochastic Process. Appl. 37: 187–197, 1991], [Stochastic Process. Appl. 68: 285–302, 1997] for multidimensional equations. Finally, we also discuss the homogeneous case.

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2

Leemans, Sander J. J., Wil M. P. van der Aalst, Tobias Brockhoff, and Artem Polyvyanyy. "Stochastic process mining: Earth movers’ stochastic conformance." Information Systems 102 (December 2021): 101724. http://dx.doi.org/10.1016/j.is.2021.101724.

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3

Sembiring, Jaka, Alireza S. Sabzevary, and Kageo Akizuki. "STOCHASTIC PROCESS ON MULTIWAVELET." IFAC Proceedings Volumes 35, no. 1 (2002): 211–15. http://dx.doi.org/10.3182/20020721-6-es-1901.00446.

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4

Yoshida, Hiroaki, Katsuhito Yamaguchi, and Yoshio Ishikawa. "Stochastic Process Optimization Technique." Applied Mathematics 05, no. 19 (2014): 3079–90. http://dx.doi.org/10.4236/am.2014.519293.

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5

Wang, Wenhua, and Hongyu Wang. "A research on segmentation of nonstationary stochastic process into piecewise stationary stochastic process." Journal of Electronics (China) 14, no. 4 (October 1997): 304–10. http://dx.doi.org/10.1007/s11767-997-0003-6.

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6

Doosti, H., M. Afshari, and H. A. Niroumand. "Wavelets for Nonparametric Stochastic Regression with Mixing Stochastic Process." Communications in Statistics - Theory and Methods 37, no. 3 (January 30, 2008): 373–85. http://dx.doi.org/10.1080/03610920701653003.

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7

Stojanovic, Vladica, Biljana Popovic, and Predrag Popovic. "Stochastic analysis of GSB process." Publications de l'Institut Math?matique (Belgrade) 95, no. 109 (2014): 149–59. http://dx.doi.org/10.2298/pim1409149s.

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We present a modification (and partly a generalization) of STOPBREAK process, which is the stochastic model of time series with permanent, emphatic fluctuations. The threshold regime of the process is obtained by using, so called, noise indicator. Now, the model, named the General Split- BREAK (GSB) process, is investigated in terms of its basic stochastic properties. We analyze some necessary and sufficient conditions of the existence of stationary GSB process, and we describe its correlation structure. Also, we define the sequence of the increments of the GSB process, named Split-MA process. Besides the standard investigation of stochastic properties of this process, we also give the conditions of its invertibility.

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8

Zhou, Xiao Qin, Wen Cai Wang, and Hong Wei Zhao. "Moment Stability of Stochastic Regenerative Cutting Process." Advanced Materials Research 97-101 (March 2010): 3038–41. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.3038.

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The stochastic uncertainties of regenerative cutting process (RCP) are taken into consideration, and both cutting stiffness and damping coefficients are modeled as two stationary stochastic processes. The eigenvalue equations are established for the stability analysis of stochastic RCP, corresponding to the differential equations of the first and second order moments. Thus the stability analysis of stochastic RCP is transformed into that of the first two order moments. The influence of stochastic uncertainties on the cutting stability of RCP is discussed. The numerical experiments have verified that with the increase of stochastic uncertainties, the cutting stability boundary was shifted downwards significantly, and the number of lobes was also multiplied.

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9

Kazakova, Tamara A. "Translation as Stochastic Informational Process." Journal of Siberian Federal University. Humanities & Social Sciences 9, no. 3 (March 2016): 536–42. http://dx.doi.org/10.17516/1997-1370-2016-9-3-536-542.

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10

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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11

Vitali, Giuliano. "Runoff as a Stochastic Process." International Journal of Mathematics and Computers in Simulation 16 (March 8, 2022): 59–62. http://dx.doi.org/10.46300/9102.2022.16.9.

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Runoff stationary critical flow is investigated as a stochastic process by means of two routing simulation models, a stream confluence, which has beens interpreted as a Marcus- Lushnikov coalescence process, and a channel splitting model, which has ben interpreted as a Markov chain over a regular tree. Despite of the expected similarity due to expection that they should be seen as one the backward of the other, the initiation and the stopping methods using in algorithms influence strongly stream size distribution.

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12

Park, M., and M. V. Tretyakov. "Stochastic Resin Transfer Molding Process." SIAM/ASA Journal on Uncertainty Quantification 5, no. 1 (January 2017): 1110–35. http://dx.doi.org/10.1137/16m1096578.

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13

Bolle, Friedel, and Philipp E. Otto. "Matching as a Stochastic Process." Jahrbücher für Nationalökonomie und Statistik 236, no. 3 (May 1, 2016): 323–48. http://dx.doi.org/10.1515/jbnst-2015-1017.

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Abstract Results of multi-party bargaining are usually described by concepts from cooperative game theory, in particular by the core. In one-on-one matching, core allocations are stable in the sense that no pair of unmatched or otherwise matched players can improve their incomes by forming a match. Because of incomplete information and bounded rationality, it is difficult to adopt a core allocation immediately. Theoretical investigations cope with the problem of whether core allocations can be adopted in a stochastic process with repeated re-matching. In this paper, we investigate sequences of matching with data from an experimental 2×2 labor market with wage negotiations. This market has seven possible matching structures (states) and is additionally characterized by the negotiated wages and profits. First, we describe the stochastic process of transitions from one state to another including the average transition times. Second, we identify different influences on the process parameters as, for example, the difference of incomes in a match. Third, allocations in the core should be completely durable or at least more durable than comparable out-of-core allocations, but they are not. Final bargaining results (induced by a time limit) appear as snapshots of a stochastic process without absorbing states and with only weak systematic influences.

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14

Dubinsky, J. M. "EXCITOTOXICITY AS A STOCHASTIC PROCESS." Clinical and Experimental Pharmacology and Physiology 22, no. 4 (April 1995): 297–98. http://dx.doi.org/10.1111/j.1440-1681.1995.tb02001.x.

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15

Sembiring, Jaka, and Kageo Akizuki. "Stochastic Process on CL Multiwavelet." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2001 (May 5, 2001): 271–74. http://dx.doi.org/10.5687/sss.2001.271.

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16

Hu, Inchi, and Chi-Wen Jevons Lee. "Bayesian Adaptive Stochastic Process Termination." Mathematics of Operations Research 28, no. 2 (May 2003): 361–81. http://dx.doi.org/10.1287/moor.28.2.361.14481.

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17

Gu, Mengyang, Xiaojing Wang, and James O. Berger. "Robust Gaussian stochastic process emulation." Annals of Statistics 46, no. 6A (December 2018): 3038–66. http://dx.doi.org/10.1214/17-aos1648.

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18

Dondi, Francesco, Alberto Cavazzini, and Luisa Pasti. "Chromatography as Lévy Stochastic process." Journal of Chromatography A 1126, no. 1-2 (September 2006): 257–67. http://dx.doi.org/10.1016/j.chroma.2006.06.030.

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19

Ehm, Werner. "A Riemann zeta stochastic process." Comptes Rendus Mathematique 345, no. 5 (September 2007): 279–82. http://dx.doi.org/10.1016/j.crma.2007.07.023.

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20

Enoki, M., Y. Utoh, and T. Kishi. "Stochastic microfracture process of ceramics." Materials Science and Engineering: A 176, no. 1-2 (March 1994): 289–93. http://dx.doi.org/10.1016/0921-5093(94)90988-1.

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21

Ejima, Toshiaki. "Convergence of stochastic relaxation process." Electronics and Communications in Japan (Part I: Communications) 71, no. 9 (September 1988): 36–43. http://dx.doi.org/10.1002/ecja.4410710905.

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22

Antsiperov, Viacheslav. "Point Process Intensity Shape Identification Based on Available Precedents Stochastic Descriptions." International Journal of Signal Processing Systems 7, no. 3 (September 2019): 103–6. http://dx.doi.org/10.18178/ijsps.7.3.103-106.

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23

Han, Linghui, Huijun Sun, David Z. W. Wang, and Chengjuan Zhu. "A stochastic process traffic assignment model considering stochastic traffic demand." Transportmetrica B: Transport Dynamics 6, no. 3 (October 12, 2016): 169–89. http://dx.doi.org/10.1080/21680566.2016.1240051.

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24

Rozos, Evangelos, Jörg Wieland, and Jorge Leandro. "Measuring Turbulent Flows: Analyzing a Stochastic Process with Stochastic Tools." Fluids 9, no. 6 (May 30, 2024): 128. http://dx.doi.org/10.3390/fluids9060128.

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Assessing drag force and Reynolds stresses in turbulent flows is crucial for evaluating the stability and longevity of hydraulic structures. Yet, this task is challenging due to the complex nature of turbulent flows. To address this, physical models are often employed. Nonetheless, this practice is associated with difficulties, especially in the case of high sampling frequency where the inherent randomness of velocity fluctuations becomes mixed with the measurement noise. This study introduces a stochastic approach, which aims to mitigate bias from measurement errors and provide a probabilistic estimate of extreme stress values. To accomplish this, a simple experimental setup with a hydraulic jump was employed to acquire long-duration velocity measurements. Subsequently, a modified first-order autoregressive model was applied through ensemble simulations, demonstrating the benefits of the stochastic approach. The analysis highlights its effectiveness in estimating the uncertainty of extreme events frequency and minimizing the bias induced by the noise in the high-magnitude velocity measurements and by the limited length of observations. These findings contribute to advancing our understanding of turbulent flow analysis and have implications for the design and assessment of hydraulic structures.

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25

Tang, Bao Xin, Kai Hua Cheng, and Qi Li. "Simulating Stochastic Process with a Monophyletic Random Vector." Advanced Materials Research 374-377 (October 2011): 1698–703. http://dx.doi.org/10.4028/www.scientific.net/amr.374-377.1698.

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Abstract. The large number of basic random variables in stochastic process, cause great troubles for calculation and analysis. Based on twice orthogonal expansion in the stochastic process and the expression of uncorrelated random vectors by use of orthogonal functions originate from a single source random variable, a method of triple orthogonal expansion for a stochastic process is put forward ,which can simulate a stochastic process with only one random variable. Example calculation shows the effectiveness of the monophyletic analysis method (MAM).This method can be applied for the other stochastic analysis based on the correlation theory.

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26

Minh, Vu Trieu, Nitin Afzulpurkar, and W. M. Wan Muhamad. "Fault Detection and Control of Process Systems." Mathematical Problems in Engineering 2007 (2007): 1–20. http://dx.doi.org/10.1155/2007/80321.

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This paper develops a stochastic hybrid model-based control system that can determine online the optimal control actions, detect faults quickly in the control process, and reconfigure the controller accordingly using interacting multiple-model (IMM) estimator and generalized predictive control (GPC) algorithm. A fault detection and control system consists of two main parts: the first is the fault detector and the second is the controller reconfiguration. This work deals with three main challenging issues: design of fault model set, estimation of stochastic hybrid multiple models, and stochastic model predictive control of hybrid multiple models. For the first issue, we propose a simple scheme for designing faults for discrete and continuous random variables. For the second issue, we consider and select a fast and reliable fault detection system applied to the stochastic hybrid system. Finally, we develop a stochastic GPC algorithm for hybrid multiple-models controller reconfiguration with soft switching signals based on weighted probabilities. Simulations for the proposed system are illustrated and analyzed.

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27

TUDOROIU, Elena-Roxana, Sorin-Mihai RADU, Wilhelm KECS, and Nicolae ILIAS. "STOCHASTIC OPTIMAL CONTROL OF pH NEUTRALISATION PROCESS IN A WATER TREATMENT PLANT." Review of the Air Force Academy 15, no. 1 (May 22, 2017): 49–68. http://dx.doi.org/10.19062/1842-9238.2017.15.1.7.

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28

Whitt, Ward. "The renewal-process stationary-excess operator." Journal of Applied Probability 22, no. 1 (March 1985): 156–67. http://dx.doi.org/10.2307/3213755.

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This paper describes the operator mapping a renewal-interval distribution into its associated stationary-excess distribution. This operator is monotone for some kinds of stochastic order, but not for the usual stochastic order determined by the expected value of all non-decreasing functions. Conditions for a renewal-interval distribution to be larger or smaller than its associated stationary-excess distribution for several kinds of stochastic order are determined in terms of familiar notions of ageing. Convergence results are also obtained for successive iterates of the operator, which supplement Harkness and Shantaram (1969), (1972) and van Beek and Braat (1973).

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29

Whitt, Ward. "The renewal-process stationary-excess operator." Journal of Applied Probability 22, no. 01 (March 1985): 156–67. http://dx.doi.org/10.1017/s0021900200029089.

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This paper describes the operator mapping a renewal-interval distribution into its associated stationary-excess distribution. This operator is monotone for some kinds of stochastic order, but not for the usual stochastic order determined by the expected value of all non-decreasing functions. Conditions for a renewal-interval distribution to be larger or smaller than its associated stationary-excess distribution for several kinds of stochastic order are determined in terms of familiar notions of ageing. Convergence results are also obtained for successive iterates of the operator, which supplement Harkness and Shantaram (1969), (1972) and van Beek and Braat (1973).

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30

Froot, Kenneth A., and Maurice Obstfeld. "Stochastic Process Switching: Some Simple Solutions." Econometrica 59, no. 1 (January 1991): 241. http://dx.doi.org/10.2307/2938249.

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31

Lotov, V. I., and V. R. Xodjibayev. "On a stochastic process with switchings." Sibirskie Elektronnye Matematicheskie Izvestiya 16 (October 21, 2019): 1531–46. http://dx.doi.org/10.33048/semi.2019.16.104.

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32

Bidabad, Bijan, and Behrouz Bidabad. "Complex Probability and Markov Stochastic Process." Indian Journal of Finance and Banking 3, no. 1 (June 4, 2019): 13–22. http://dx.doi.org/10.46281/ijfb.v3i1.290.

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This note discusses the existence of "complex probability" in the real world sensible problems. By defining a measure more general than the conventional definition of probability, the transition probability matrix of discrete Markov chain is broken to the periods shorter than a complete step of the transition. In this regard, the complex probability is implied.

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33

S. K. Sahoo, S. K. Sahoo. "Mathematical Finance: Applications of Stochastic Process." IOSR Journal of Mathematics 2, no. 2 (2012): 38–42. http://dx.doi.org/10.9790/5728-0223842.

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34

Ferreira, R. M. S. "A Scaling Method for Stochastic Process." Acta Physica Polonica B 46, no. 6 (2015): 1143. http://dx.doi.org/10.5506/aphyspolb.46.1143.

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35

Sampson, D. B. "Fish capture as a stochastic process." ICES Journal of Marine Science 45, no. 1 (January 1, 1988): 39–60. http://dx.doi.org/10.1093/icesjms/45.1.39.

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36

Singh, Prashant. "Random acceleration process under stochastic resetting." Journal of Physics A: Mathematical and Theoretical 53, no. 40 (September 17, 2020): 405005. http://dx.doi.org/10.1088/1751-8121/abaf2d.

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37

Istas, Jacques, and Catherine Laredo. "Estimating Functionals of a Stochastic Process." Advances in Applied Probability 29, no. 1 (March 1997): 249–70. http://dx.doi.org/10.2307/1427869.

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The problem of estimating the integral of a stochastic process from observations at a finite number N of sampling points has been considered by various authors. Recently, Benhenni and Cambanis (1992) studied this problem for processes with mean 0 and Hölder index K + ½, K ; ℕ These results are here extended to processes with arbitrary Hölder index. The estimators built here are linear in the observations and do not require the a priori knowledge of the smoothness of the process. If the process satisfies a Hölder condition with index s in quadratic mean, we prove that the rate of convergence of the mean square error is N2s+1 as N goes to ∞, and build estimators that achieve this rate.

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38

John, Sarah, and John W. Wilson. "Quantum dynamics as a stochastic process." Physical Review E 49, no. 1 (January 1, 1994): 145–56. http://dx.doi.org/10.1103/physreve.49.145.

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39

van Dorsselaer, F. E., and G. Nienhuis. "Collective decay as a stochastic process." Physical Review A 56, no. 1 (July 1, 1997): 958–66. http://dx.doi.org/10.1103/physreva.56.958.

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40

Murata, Tsutomu, Nobuyuki Matsui, Satoru Miyauchi, Yuki Kakita, and Toshio Yanagida. "Discrete stochastic process underlying perceptual rivalry." NeuroReport 14, no. 10 (July 2003): 1347–52. http://dx.doi.org/10.1097/01.wnr.0000077553.91466.41.

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41

Aytulun, S. K., and A. F. Guneri. "Business process modelling with stochastic networks." International Journal of Production Research 46, no. 10 (May 15, 2008): 2743–64. http://dx.doi.org/10.1080/00207540701543601.

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42

Nakatsuka, Toshinao. "Absorbing process in recursive stochastic equations." Journal of Applied Probability 35, no. 2 (June 1998): 418–26. http://dx.doi.org/10.1239/jap/1032192857.

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We introduce the concept of the absorbing process for analysing a state process. Our aim is to show the existence of the absorbing process with probability one. This process is shown to be stationary, asymptotically stationary, periodic or a.m.s., if the input distribution has such properties. The real process is absorbed into this process so that its stability and some other properties are easily derived.

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43

Xu, Jason, Yiwen Wang, Peter Guttorp, and Janis L. Abkowitz. "Visualizing hematopoiesis as a stochastic process." Blood Advances 2, no. 20 (October 16, 2018): 2637–45. http://dx.doi.org/10.1182/bloodadvances.2018023705.

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Abstract Stochastic simulation has played an important role in understanding hematopoiesis, but implementing and interpreting mathematical models requires a strong statistical background, often preventing their use by many clinical and translational researchers. Here, we introduce a user-friendly graphical interface with capabilities for visualizing hematopoiesis as a stochastic process, applicable to a variety of mammal systems and experimental designs. We describe the visualization tool and underlying mathematical model, and then use this to simulate serial transplantations in mice, human cord blood cell expansion, and clonal hematopoiesis of indeterminate potential. The outcomes of these virtual experiments challenge previous assumptions and provide examples of the flexible range of hypotheses easily testable via the visualization tool.

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44

Khrennikov, A. "Entanglement's dynamics from classical stochastic process." EPL (Europhysics Letters) 88, no. 4 (November 1, 2009): 40005. http://dx.doi.org/10.1209/0295-5075/88/40005.

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45

Murata, Tsutomu, Nobuyuki Matsui, Satoru Miyauchi, Yuki Kakita, and Toshio Yanagida. "Discrete stochastic process underlying perceptual rivalry." NeuroReport 14, no. 10 (July 2003): 1347–52. http://dx.doi.org/10.1097/00001756-200307180-00013.

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46

Istas, Jacques, and Catherine Laredo. "Estimating Functionals of a Stochastic Process." Advances in Applied Probability 29, no. 01 (March 1997): 249–70. http://dx.doi.org/10.1017/s0001867800027877.

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Abstract:

The problem of estimating the integral of a stochastic process from observations at a finite number N of sampling points has been considered by various authors. Recently, Benhenni and Cambanis (1992) studied this problem for processes with mean 0 and Hölder index K + ½, K ; ℕ These results are here extended to processes with arbitrary Hölder index. The estimators built here are linear in the observations and do not require the a priori knowledge of the smoothness of the process. If the process satisfies a Hölder condition with index s in quadratic mean, we prove that the rate of convergence of the mean square error is N 2s+1 as N goes to ∞, and build estimators that achieve this rate.

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47

Nakatsuka, Toshinao. "Absorbing process in recursive stochastic equations." Journal of Applied Probability 35, no. 02 (June 1998): 418–26. http://dx.doi.org/10.1017/s0021900200015047.

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We introduce the concept of the absorbing process for analysing a state process. Our aim is to show the existence of the absorbing process with probability one. This process is shown to be stationary, asymptotically stationary, periodic or a.m.s., if the input distribution has such properties. The real process is absorbed into this process so that its stability and some other properties are easily derived.

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48

Petrović, Ljiljana. "Markovian extensions of a stochastic process." Statistics & Probability Letters 78, no. 6 (April 2008): 810–14. http://dx.doi.org/10.1016/j.spl.2007.09.048.

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49

Dougherty, Ed M. "Is human failure a stochastic process?" Reliability Engineering & System Safety 55, no. 3 (March 1997): 209–15. http://dx.doi.org/10.1016/s0951-8320(96)00122-6.

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50

Glynn, Peter, and Karl Sigman. "Independent sampling of a stochastic process." Stochastic Processes and their Applications 74, no. 2 (June 1998): 151–64. http://dx.doi.org/10.1016/s0304-4149(97)00114-2.

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