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1

Kaňková, Vlasta. "Multistage Stochastic Decision and Economic Processes." Acta Oeconomica Pragensia 13, no. 1 (2005): 119–27. http://dx.doi.org/10.18267/j.aop.143.

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2

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 (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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3

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

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4

Zhang, Yingqi, Wei Cheng, Xiaowu Mu та Caixia Liu. "Stochasticℋ∞Finite-Time Control of Discrete-Time Systems with Packet Loss". Mathematical Problems in Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/897481.

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This paper investigates the stochastic finite-time stabilization andℋ∞control problem for one family of linear discrete-time systems over networks with packet loss, parametric uncertainties, and time-varying norm-bounded disturbance. Firstly, the dynamic model description studied is given, which, if the packet dropout is assumed to be a discrete-time homogenous Markov process, the class of discrete-time linear systems with packet loss can be regarded as Markovian jump systems. Based on Lyapunov function approach, sufficient conditions are established for the resulting closed-loop discrete-time system with Markovian jumps to be stochasticℋ∞finite-time boundedness and then state feedback controllers are designed to guarantee stochasticℋ∞finite-time stabilization of the class of stochastic systems. The stochasticℋ∞finite-time boundedness criteria can be tackled in the form of linear matrix inequalities with a fixed parameter. As an auxiliary result, we also give sufficient conditions on the robust stochastic stabilization of the class of linear systems with packet loss. Finally, simulation examples are presented to illustrate the validity of the developed scheme.
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5

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 (2017): 49–68. http://dx.doi.org/10.19062/1842-9238.2017.15.1.7.

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6

Osadchiy, V. I., N. N. Osadcha, L. A. Kovalchuk, and O. Y. Skrynyk. "The stochastic process of changing the concentration of ammonia nitrogen in the Desna river." Reports of the National Academy of Sciences of Ukraine, no. 10 (November 16, 2016): 60–66. http://dx.doi.org/10.15407/dopovidi2016.10.060.

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7

Polyakova, A. Yu. "Feat ures of continuing formati on of stochastic cult ure of schoolchildren in the conditions of dista nce learning." Informatics in school, no. 6 (September 25, 2021): 39–48. http://dx.doi.org/10.32517/2221-1993-2021-20-6-39-48.

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The article describes the essence of continuity in the formation of a stochastic culture of students in the conditions of distance learning. The author’s definition of the concept “continuity in the formation of stochastic culture of students of a general education school in the conditions of distance learning” is given. There are the basic conditions of a teacher’s possession of the highest level of stochastic culture, due to which the formation of an integral personal quality, which is a generalized indicator of stochastic competence, takes place in schoolchildren. Modern information and communication technologies used in a series of developed distance classes in statistics, combinatorics and probability theory for grade 11 students are proposed. These ICT contribute to the successive formation of elements of stochastic culture in the conditions of distance learning. The article emphasizes that the use of ICT in teaching stochastics is an effective methodological tool, through which significant results can be achieved in the educational process.
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8

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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9

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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10

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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11

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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12

Argent-Katwala, Ashok, Jeremy T. Bradley, and Nicholas J. Dingle. "Expressing performance requirements using regular expressions to specify stochastic probes over process algebra models." ACM SIGSOFT Software Engineering Notes 29, no. 1 (2004): 49–58. http://dx.doi.org/10.1145/974043.974051.

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13

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 (1997): 304–10. http://dx.doi.org/10.1007/s11767-997-0003-6.

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14

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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15

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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16

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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17

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 (2008): 373–85. http://dx.doi.org/10.1080/03610920701653003.

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18

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

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19

Lee, P. M., and Byron S. Gottfried. "Elements of Stochastic Process Simulation." Mathematical Gazette 69, no. 447 (1985): 64. http://dx.doi.org/10.2307/3616475.

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20

Bolle, Friedel, and Philipp E. Otto. "Matching as a Stochastic Process." Jahrbücher für Nationalökonomie und Statistik 236, no. 3 (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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21

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

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22

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

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23

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

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24

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

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25

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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26

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

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27

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

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28

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

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29

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

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30

Whitt, Ward. "The renewal-process stationary-excess operator." Journal of Applied Probability 22, no. 1 (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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31

Whitt, Ward. "The renewal-process stationary-excess operator." Journal of Applied Probability 22, no. 01 (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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32

Sánchez-López, Borja, and Jesus Cerquides. "On the Convergence of Stochastic Process Convergence Proofs." Mathematics 9, no. 13 (2021): 1470. http://dx.doi.org/10.3390/math9131470.

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Convergence of a stochastic process is an intrinsic property quite relevant for its successful practical for example for the function optimization problem. Lyapunov functions are widely used as tools to prove convergence of optimization procedures. However, identifying a Lyapunov function for a specific stochastic process is a difficult and creative task. This work aims to provide a geometric explanation to convergence results and to state and identify conditions for the convergence of not exclusively optimization methods but any stochastic process. Basically, we relate the expected directions set of a stochastic process with the half-space of a conservative vector field, concepts defined along the text. After some reasonable conditions, it is possible to assure convergence when the expected direction resembles enough to some vector field. We translate two existent and useful convergence results into convergence of processes that resemble to particular conservative vector fields. This geometric point of view could make it easier to identify Lyapunov functions for new stochastic processes which we would like to prove its convergence.
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33

Dshalalow, Jewgeni H., and Jean-Baptiste Bacot. "On functionals of a marked Poisson process observed by a renewal process." International Journal of Mathematics and Mathematical Sciences 26, no. 7 (2001): 427–36. http://dx.doi.org/10.1155/s0161171201005221.

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We study the functionals of a Poisson marked processΠobserved by a renewal process. A sequence of observations continues untilΠcrosses some fixed level at one of the observation epochs (the first passage time). In various stochastic models applications (such as queueing withN-policy combined with multiple vacations), it is necessary to operate with the value ofΠprior to the first passage time, or prior to the first passage time plus some random time. We obtain a time-dependent solution to this problem in a closed form, in terms of its Laplace transform. Many results are directly applicable to the time-dependent analysis of queues and other stochastic models via semi-regenerative techniques.
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34

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 (2016): 169–89. http://dx.doi.org/10.1080/21680566.2016.1240051.

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35

Lykova, Kseniya. "The Model of Formation of a Stochastic Worldview of High School Students in the Context of the Digitalization of Mathematics Education." Profession-Oriented School 9, no. 2 (2021): 53–59. http://dx.doi.org/10.12737/1998-0744-2021-9-2-53-59.

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The article is devoted to the study of the peculiarities of the stochastic worldview of high school students. Stochastic world outlook lays the landmarks of creative and practical activities, helps to create favorable conditions for development of intellectual potential of a pupil. 
 Digitalization in education determines the dominant nature of both the active use of modern methods, tools and technologies of education, and the formation of students' digital skills, training in data processing and analysis, which is closely related to the teaching of stochastics and indicates the relevance of research. In this regard, the untapped potential of stochasticity is greater than any other field of knowledge. Widely used probabilistic-statistical methods contribute to the study of variability and complexity of political, economic, social processes. In this regard, a model of formation of stochastic worldview of high school students in the conditions of digitalization of mathematical education is developed. This model is a set of educational and digital information content, which gives the results of the educational process a new quality of education, allows you to realize the integrity of the worldview, values and motivational constructs, creates the necessary conditions under which it becomes possible to generate knowledge by students themselves based on self-development and self-actualization, their active and productive creativity.
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36

Nieto, Fabio, and Edna C. Moreno. "Univariate Conditional Distributions of an Open-Loop TAR Stochastic Process." Revista Colombiana de Estadística 39, no. 2 (2016): 149. http://dx.doi.org/10.15446/rce.v39n2.58912.

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<p>Clusters of large values are observed in sample paths of certain open-loop threshold autoregressive (TAR) stochastic processes. In order to characterize the stochastic mechanism that generates this empirical stylized fact, three types of marginal conditional distributions of the underlying stochastic process are analyzed in this paper. One allows us to find the conditional variance function that explains the aforementioned stylized fact. As a by-product, we are able to derive a sufficient condition to have asymptotic weak stationarity in an open-loop TAR stochastic process.</p>
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37

Amemori, Ken-ichi, and Shin Ishii. "Gaussian Process Approach to Spiking Neurons for Inhomogeneous Poisson Inputs." Neural Computation 13, no. 12 (2001): 2763–97. http://dx.doi.org/10.1162/089976601317098529.

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This article presents a new theoretical framework to consider the dynamics of a stochastic spiking neuron model with general membrane response to input spike. We assume that the input spikes obey an inhomogeneous Poisson process. The stochastic process of the membrane potential then becomes a gaussian process. When a general type of the membrane response is assumed, the stochastic process becomes a Markov-gaussian process. We present a calculation method for the membrane potential density and the firing probability density. Our new formulation is the extension of the existing formulation based on diffusion approximation. Although the single Markov assumption of the diffusion approximation simplifies the stochastic process analysis, the calculation is inaccurate when the stochastic process involves a multiple Markov property. We find that the variation of the shape of the membrane response, which has often been ignored in existing stochastic process studies, significantly affects the firing probability. Our approach can consider the reset effect, which has been difficult to deal with by analysis based on the first passage time density.
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38

Bakosi, J., and J. R. Ristorcelli. "A Stochastic Diffusion Process for the Dirichlet Distribution." International Journal of Stochastic Analysis 2013 (April 10, 2013): 1–7. http://dx.doi.org/10.1155/2013/842981.

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The method of potential solutions of Fokker-Planck equations is used to develop a transport equation for the joint probability of N coupled stochastic variables with the Dirichlet distribution as its asymptotic solution. To ensure a bounded sample space, a coupled nonlinear diffusion process is required: the Wiener processes in the equivalent system of stochastic differential equations are multiplicative with coefficients dependent on all the stochastic variables. Individual samples of a discrete ensemble, obtained from the stochastic process, satisfy a unit-sum constraint at all times. The process may be used to represent realizations of a fluctuating ensemble of N variables subject to a conservation principle. Similar to the multivariate Wright-Fisher process, whose invariant is also Dirichlet, the univariate case yields a process whose invariant is the beta distribution. As a test of the results, Monte Carlo simulations are used to evolve numerical ensembles toward the invariant Dirichlet distribution.
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39

Eom, Young Ho, and Woon Wook Jang. "An Analysis on the Stochastic Process underlying KOSPI200 Index Oprions Focused on Jumps Process." Journal of Derivatives and Quantitative Studies 23, no. 2 (2015): 183–205. http://dx.doi.org/10.1108/jdqs-02-2015-b0002.

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This paper investigates empirically the modelling issues for the stochastic processes underlying KOSPI200 index options. Empirical results show that we need to incorporate two factor stochastic volatility processes to have a good option pricing performance. However, the number of the leverage channel is not an important issue for the modelling of the KOSPI200 index options. Our results also show that the models with finite activity large jumps outperform that with infinite activity small jumps for the financial crisis period. On the while, for the pre-crisis period, there is no clear superiority or inferiority between both jumps models.
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40

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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41

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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42

Bidabad, Bijan, and Behrouz Bidabad. "Complex Probability and Markov Stochastic Process." Indian Journal of Finance and Banking 3, no. 1 (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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43

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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44

Nakatsuka, Toshinao. "Absorbing process in recursive stochastic equations." Journal of Applied Probability 35, no. 02 (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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45

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

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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 N 2s+1 as N goes to ∞, and build estimators that achieve this rate.
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46

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

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47

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

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48

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

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49

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

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

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