Relevant bibliographies by topics / Stochastic process

Academic literature on the topic 'Stochastic process'

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Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Stochastic process"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Stochastic process"

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PEREIRA, RICARDO VELA DE BRITTO. "VOLATILITY: A HIDDEN STOCHASTIC PROCESS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16816@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
A volatilidade é um parâmetro importante de modelagem do mercado financeiro. Ela controla a medida de risco associado à dinâmica estocástica de preço do título financeiro, afetando também o preço racional dos derivativos.Existe evidência empírica que a volatilidade é por sua vez também um processo estocástico, subjacente ao dos preços. Assim, a volatilidade não pode ser observada diretamente e tem que ser estimada, constituindo-se de um processo estocástico escondido.Nesta dissertação, consideramos um estimador para a volatilidade diária do índice da BOVESPA, baseado em banco de dados intradiários. Fazemos uma análise estatística descritiva da série temporal obtida, obtendo-se a função densidade de probabilidade, os momentos e as correlações. Comparamos os resultados empíricos com as previsões teóricas de vários modelos de volatilidade estocástica. Consideramos a classe de equações de Itô-Langevin formada por um processo de reversão à média e um processo difusivo de Wiener generalizado, com componentes de ruído multiplicativo e/ou aditivo. A partir dessa análise, é sugerido um modelo para descrever as flutuações de volatilidade dos preços do mercado acionário brasileiro.
Volatility is a key model parameter of the financial market. It controls the risk associated to the stochastic dynamics of the asset prices and also affects the rational price of derivative products. There are empirical evidences that the volatility is also a stochastic process, underlined to the price one. Therefore, the volatility is not directly observed and must be estimated, constituting a hidden stochastic process. In this work, we consider an estimate for the daily volatility of the BOVESPA index, computed from the intraday database. We perform a descriptive statistical analysis of the resulting time series, obtaining the probability density function, moments and correlations. We compare the empirical outcomes with the theoretical forecasts of many stochastic volatility models. We consider the class of Itô-Langevin equations composed by a mean reverting process and a generalized diffusive Wiener process with multiplicative and/or additive noise components. From this analysis, we propose a model that describes the volatility fluctuations of the Brazilian stock market.
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Catalão, André Borges [UNESP]. "Modelagem estocástica de opções de câmbio no Brasil: aplicação de transformada rápida de Fourier e expansão assintótica ao modelo de Heston." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/88592.

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Made available in DSpace on 2014-06-11T19:23:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-12-13Bitstream added on 2014-06-13T18:09:47Z : No. of bitstreams: 1 catalao_ab_me_ift.pdf: 811288 bytes, checksum: d4e34c59801bd92233bc9f26884a19ab (MD5)
Neste trabalho estudamos a calibração de opções de câmbio no mercado brasileiro utilizando o processo estocástico proposto por Heston [Heston, 1993], como uma alternativa ao modelo de apreçamento de Black e Scholes [Black e Scholes,1973], onde as volatilidades implícitas de opções para diferentes preços de exercícios e prazos são incorporadas ad hoc. Comparamos dois métodos de apreçamento: o método de Carr e Madan [Carr e Madan, 1999], que emprega transfomada rápida de Fourier e função característica, e expansão assintótica para baixos valores de volatilidade da variância. Com a nalidade de analisar o domínio de aplicabilidade deste método, selecionamos períodos de alta volatilidade no mercado, correspondente à crise subprime de 2008, e baixa volatilidade, correspondente ao período subsequente. Adicionalmente, estudamos a incorporação de swaps de variância para melhorar a calibração do modelo
In this work we study the calibration of forex call options in the Brazilian market using the stochastic process proposed by Heston [Heston, 1993], as an alternative to the Black and Scholes [Black e Scholes,1973] pricing model, in which the implied option volatilities related to di erent strikes and maturities are incorporated in an ad hoc manner. We compare two pricing methods: one from Carr and Madan [Carr e Madan, 1999], which uses fast Fourier transform and characteristic function, and asymptotic expantion for low values of the volatility of variance. To analyze the applicability of this method, we select periods of high volatility in the market, related to the subprime crisis of 2008, and of low volatility, correspondent to the following period. In addition, we study the use of variance swaps to improve the calibration of the model
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Pihnastyi, O. M., and V. D. Khodusov. "Stochastic equation of the technological process." Thesis, Igor Sikorsky Kyiv Polytechnic Institute, 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/39059.

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This document presents the construction of a stochastic equation for the process of manufacturing products on a production line. We base our research on the synchronized production line. The minimum size of the inter-operational storage is determined, at which the continuous production is possible. The stochastic equation of the production process is written in canonical form. The definition of the diffusion coefficient for the time of processing of subjects of labour.
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Gibellato, Marilisa Gail. "Stochastic modeling of the sleep process." The Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1110318321.

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Gibellato, M. G. "Stochastic modeling of the sleep process." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1110318321.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xvii, 188 p.; also includes graphics Includes bibliographical references (p. 184-188). Available online via OhioLINK's ETD Center
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Bohnenkamp, Henrik. "Compositional solution of stochastic process algebra models." [S.l.] : [s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=965593193.

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Rogge-Solti, Andreas, Ronny S. Mans, der Aalst Wil M. P. van, and Mathias Weske. "Repairing event logs using stochastic process models." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6679/.

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Companies strive to improve their business processes in order to remain competitive. Process mining aims to infer meaningful insights from process-related data and attracted the attention of practitioners, tool-vendors, and researchers in recent years. Traditionally, event logs are assumed to describe the as-is situation. But this is not necessarily the case in environments where logging may be compromised due to manual logging. For example, hospital staff may need to manually enter information regarding the patient’s treatment. As a result, events or timestamps may be missing or incorrect. In this paper, we make use of process knowledge captured in process models, and provide a method to repair missing events in the logs. This way, we facilitate analysis of incomplete logs. We realize the repair by combining stochastic Petri nets, alignments, and Bayesian networks. We evaluate the results using both synthetic data and real event data from a Dutch hospital.
Unternehmen optimieren ihre Geschäftsprozesse laufend um im kompetitiven Umfeld zu bestehen. Das Ziel von Process Mining ist es, bedeutende Erkenntnisse aus prozessrelevanten Daten zu extrahieren. In den letzten Jahren sorgte Process Mining bei Experten, Werkzeugherstellern und Forschern zunehmend für Aufsehen. Traditionell wird dabei angenommen, dass Ereignisprotokolle die tatsächliche Ist-Situation widerspiegeln. Dies ist jedoch nicht unbedingt der Fall, wenn prozessrelevante Ereignisse manuell erfasst werden. Ein Beispiel hierfür findet sich im Krankenhaus, in dem das Personal Behandlungen meist manuell dokumentiert. Vergessene oder fehlerhafte Einträge in Ereignisprotokollen sind in solchen Fällen nicht auszuschließen. In diesem technischen Bericht wird eine Methode vorgestellt, die das Wissen aus Prozessmodellen und historischen Daten nutzt um fehlende Einträge in Ereignisprotokollen zu reparieren. Somit wird die Analyse unvollständiger Ereignisprotokolle erleichtert. Die Reparatur erfolgt mit einer Kombination aus stochastischen Petri Netzen, Alignments und Bayes'schen Netzen. Die Ergebnisse werden mit synthetischen Daten und echten Daten eines holländischen Krankenhauses evaluiert.
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Kabouris, John C. "Stochastic control of the activated sludge process." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/20306.

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Tribastone, Mirco. "Scalable analysis of stochastic process algebra models." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4629.

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The performance modelling of large-scale systems using discrete-state approaches is fundamentally hampered by the well-known problem of state-space explosion, which causes exponential growth of the reachable state space as a function of the number of the components which constitute the model. Because they are mapped onto continuous-time Markov chains (CTMCs), models described in the stochastic process algebra PEPA are no exception. This thesis presents a deterministic continuous-state semantics of PEPA which employs ordinary differential equations (ODEs) as the underlying mathematics for the performance evaluation. This is suitable for models consisting of large numbers of replicated components, as the ODE problem size is insensitive to the actual population levels of the system under study. Furthermore, the ODE is given an interpretation as the fluid limit of a properly defined CTMC model when the initial population levels go to infinity. This framework allows the use of existing results which give error bounds to assess the quality of the differential approximation. The computation of performance indices such as throughput, utilisation, and average response time are interpreted deterministically as functions of the ODE solution and are related to corresponding reward structures in the Markovian setting. The differential interpretation of PEPA provides a framework that is conceptually analogous to established approximation methods in queueing networks based on meanvalue analysis, as both approaches aim at reducing the computational cost of the analysis by providing estimates for the expected values of the performance metrics of interest. The relationship between these two techniques is examined in more detail in a comparison between PEPA and the Layered Queueing Network (LQN) model. General patterns of translation of LQN elements into corresponding PEPA components are applied to a substantial case study of a distributed computer system. This model is analysed using stochastic simulation to gauge the soundness of the translation. Furthermore, it is subjected to a series of numerical tests to compare execution runtimes and accuracy of the PEPA differential analysis against the LQN mean-value approximation method. Finally, this thesis discusses the major elements concerning the development of a software toolkit, the PEPA Eclipse Plug-in, which offers a comprehensive modelling environment for PEPA, including modules for static analysis, explicit state-space exploration, numerical solution of the steady-state equilibrium of the Markov chain, stochastic simulation, the differential analysis approach herein presented, and a graphical framework for model editing and visualisation of performance evaluation results.
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Pathmanathan, S. "The poisson process in quantum stochastic calculus." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249564.

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Given a compensated Poisson process $(X_t)_{t \geq 0}$ based on $(\Omega, \mathcal{F}, \mathbb{P})$, the Wiener-Poisson isomorphism $\mathcal{W} : \mathfrak{F}_+(L^2 (\mathbb{R}_+)) \to L^2 (\Omega, \mathcal{F}, \mathbb{P})$ is constructed. We restrict the isomorphism to $\mathfrak{F}_+(L^2 [0,1])$ and prove some novel properties of the Poisson exponentials $\mathcal{E}(f) := \mathcal{W}(e(f))$. A new proof of the result $\Lambda_t + A_t + A^{\dagger}_t = \mathcal{W}^{-1}\widehat{X_t} \mathcal{W}$ is also given. The analogous results for $\mathfrak{F}_+(L^2 (\mathbb{R}_+))$ are briefly mentioned. The concept of a compensated Poisson process over $\mathbb{R}_+$ is generalised to any measure space $(M, \mathcal{M}, \mu)$ as an isometry $I : L^2(M, \mathcal{M}, \mu) \to L^2 (\Omega,\mathcal{F}, \mathbb{P})$ satisfying certain properties. For such a generalised Poisson process we recall the construction of the generalised Wiener-Poisson isomorphism, $\mathcal{W}_I : \mathfrak{F}_+(L^2(M)) \to L^2 (\Omega, \mathcal{F}, \mathbb{P})$, using Charlier polynomials. Two alternative constructions of $\mathcal{W}_I$ are also provided, the first using exponential vectors and then deducing the connection with Charlier polynomials, and the second using the theory of reproducing kernel Hilbert spaces. Given any measure space $(M, \mathcal{M}, \mu)$, we construct a canonical generalised Poisson process $I : L^2 (M, \mathcal{M}, \mu) \to L^2(\Delta, \mathcal{B}, \mathbb{P})$, where $\Delta$ is the maximal ideal space, with $\mathcal{B}$ the completion of its Borel $\sigma$-field with respect to $\mathbb{P}$, of a $C^*$-algebra $\mathcal{A} \subseteq \mathfrak{B}(\mathfrak{F}_+(L^2(M)))$. The Gelfand transform $\mathcal{A} \to \mathfrak{B}(L^2(\Delta))$ is unitarily implemented by the Wiener-Poisson isomorphism $\mathcal{W}_I: \mathfrak{F}_+(L^2(M)) \to L^2(\Delta)$. This construction only uses operator algebra theory and makes no a priori use of Poisson measures. A new Fock space proof of the quantum Ito formula for $(\Lambda_t + A_t + A^{\dagger}_t)_{0 \leq t \leq 1}$ is given. If $(F_{\ \! \! t})_{0 \leq t \leq 1}$ is a real, bounded, predictable process with respect to a compensated Poisson process $(X_t)_{0 \leq t \leq 1}$, we show that if $M_t = \int_0^t F_s dX_s$, then on $\mathsf{E}_{\mathrm{lb}} := \mathrm{linsp} \{ e(f) : f \in L^2_{\mathrm{lb}}[0,1] \}$, $\mathcal{W}^{-1} \widehat{M_t} \mathcal{W} = \int_0^t \mathcal{W}^{-1} \widehat{F_s} \mathcal{W} (d\Lambda_s + dA_s + dA^{\dagger}_s),$ and that $(\mathcal{W}^{-1} \widehat{M_t} \mathcal{W})_{0 \leq t \leq 1}$ is an essentially self-adjoint quantum semimartingale. We prove, using the classical Ito formula, that if $(J_t)_{0 \leq t \leq 1}$ is a regular self-adjoint quantum semimartingale, then $(\mathcal{W} \widehat{M_t} \mathcal{W}^{-1} + J_t)_{0 \leq t \leq 1}$ is an essentially self-adjoint quantum semimartingale satisfying the quantum Duhamel formula, and hence the quantum Ito formula. The equivalent result for the sum of a Brownian and Poisson martingale, provided that the sum is essentially self-adjoint with core $\mathsf{E}_{\mathrm{lb}}$, is also proved.
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Books on the topic "Stochastic process"

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Whitt, Ward. Stochastic-Process Limits. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/b97479.

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Timo, Gottschalk, and Hoffmann Alex C, eds. Stochastic modelling in process technology. Amsterdam: Elsevier, 2007.

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Centrum voor Wiskunde en Informatica (Amsterdam, Netherlands), ed. Counting process systems: Identification and stochastic realization. Amsterdam, Netherlands: Centrum voor Wiskunde en Informatica, 1990.

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Stochastic-process limits: An introduction to stochastic-process limits and their application to queues. New York: Springer, 2002.

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Froot, Kenneth. Stochastic process switching: Some simple solutions. Cambridge, MA: National Bureau of Economic Research, 1989.

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O'Donnell, Joseph. The Stochastic process of interest rates. Dublin: University College Dublin, 1990.

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M, Wiper Mike, and Ríos Insua David 1964-, eds. Bayesian analysis of stochastic process models. Hoboken, New Jersey: Wiley, 2012.

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Larsen, Curtis E. Random process simulation for stochastic fatigue analysis. [Washington, D.C.]: National Aeronautics and Space Administration, 1988.

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Larsen, Curtis E. Random process simulation for stochastic fatigue analysis. [Washington, D.C.]: National Aeronautics and Space Administration, 1988.

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Segovia-Hernández, Juan Gabriel, and Fernando Israel Gómez-Castro. Stochastic Process Optimization using Aspen Plus®. Boca Raton : Taylor & Francis, CRC Press, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315155739.

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Book chapters on the topic "Stochastic process"

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Park, Kun Il. "Stochastic Process." In Fundamentals of Probability and Stochastic Processes with Applications to Communications, 135–84. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68075-0_6.

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Lanchier, Nicolas. "Logistic growth process." In Stochastic Modeling, 193–201. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50038-6_11.

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Lanchier, Nicolas. "The contact process." In Stochastic Modeling, 245–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50038-6_15.

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Masuda, Hiroki. "Stochastic Process Models." In Mathematics for Industry, 219–38. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-55060-0_17.

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Nåsell, Ingemar. "Stochastic Process Background." In Lecture Notes in Mathematics, 17–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20530-9_3.

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Esparza, Javier. "Stochastic Process Creation." In Mathematical Foundations of Computer Science 2009, 24–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03816-7_3.

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Gee, Kenneth P. "Stochastic Process Costing." In Advanced Management Accounting Problems, 10–26. London: Macmillan Education UK, 1986. http://dx.doi.org/10.1007/978-1-349-18147-6_2.

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Gee, Kenneth P. "Stochastic Process Costing." In Advanced Management Accounting Problems, 208–10. London: Macmillan Education UK, 1986. http://dx.doi.org/10.1007/978-1-349-18147-6_20.

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Gee, Kenneth P. "Stochastic Process Costing." In Advanced Management Accounting Problems, 262–65. London: Macmillan Education UK, 1986. http://dx.doi.org/10.1007/978-1-349-18147-6_38.

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Götz, N., H. Hermanns, U. Herzog, V. Mertsiotakis, and M. Rettelbach. "Stochastic Process Algebras." In Quantitative Methods in Parallel Systems, 3–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79917-4_1.

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Conference papers on the topic "Stochastic process"

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Shields, Michael D., George Deodatis, and Paolo Bocchini. "Translation Process Approximation of a General Non-Gaussian Stochastic Process." In 6th International Conference on Computational Stochastic Mechanics. Singapore: Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p054.

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Ranfagni, A., R. Ruggeri, and A. Agresti. "Tunneling as a stochastic process." In MYSTERIES, PUZZLES AND PARADOXES IN QUANTUM MECHANICS. ASCE, 1999. http://dx.doi.org/10.1063/1.57886.

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Ricordeau, A., and N. Mellouli. "A stochastic bone remodeling process." In 2008 5th IEEE International Symposium on Biomedical Imaging (ISBI 2008). IEEE, 2008. http://dx.doi.org/10.1109/isbi.2008.4541219.

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Bradley, J. T., S. T. Gilmore, and N. Thomas. "Performance analysis of stochastic process algebra models using stochastic simulation." In Proceedings 20th IEEE International Parallel & Distributed Processing Symposium. IEEE, 2006. http://dx.doi.org/10.1109/ipdps.2006.1639627.

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Cuvelier, Etienne, and Monique Noirhomme-Fraiture. "An approach to Stochastic Process using Quasi-Arithmetic Means." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0001.

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Jin, Sophie, John L. Sturtevant, Shumay Shang, Lianghong Yin, and Kevin Ahi. "Stochastic model prediction of pattern-failure." In Metrology, Inspection, and Process Control for Microlithography XXXIV, edited by Ofer Adan and John C. Robinson. SPIE, 2020. http://dx.doi.org/10.1117/12.2553235.

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Pihnastyi, Oleh, and Valery Khodusov. "Stochastic Equation of the Technological Process." In 2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC). IEEE, 2018. http://dx.doi.org/10.1109/saic.2018.8516833.

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N, Krishnadas. "Cloud Computing: Analysis using Stochastic Process." In Annual International Conference on Computer Games, Multimedia and Allied Technology. Global Science & Technology Forum (GSTF), 2013. http://dx.doi.org/10.5176/2251-1679_cgat13.33.

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Zheng, Guang, Jinzhao Wu, and Lian Li. "Stochastic Process Algebra with Value-Passing." In 2008 International Conference on Computer Science and Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/csse.2008.520.

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Zhu, Hong-bing, and Xiu Li. "Bridges Loading Course Stochastic Process Model." In First International Conference on Transportation Information and Safety (ICTIS). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41177(415)179.

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Reports on the topic "Stochastic process"

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Glynn, Peter W., and Karl Sigman. Independent Sampling of a Stochastic Process. Fort Belvoir, VA: Defense Technical Information Center, November 1991. http://dx.doi.org/10.21236/ada249712.

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Froot, Kenneth, and Maurice Obstfeld. Stochastic Process Switching: Some Simple Solutions. Cambridge, MA: National Bureau of Economic Research, June 1989. http://dx.doi.org/10.3386/w2998.

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Elliott, Robert J., and Michael Kohlmann. The Adjoint Process in Stochastic Optimal Control. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada189720.

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

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Teich, Malvin C. Analysis, Synthesis, and Estimation of Fractal-Rate Stochastic Point Process. Fort Belvoir, VA: Defense Technical Information Center, December 1997. http://dx.doi.org/10.21236/ada339241.

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Weitzman, Martin. The Ramsey Discounting Formula for a Hidden-State Stochastic Growth Process. Cambridge, MA: National Bureau of Economic Research, June 2012. http://dx.doi.org/10.3386/w18157.

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Papantoni-Kazakos, P., and Rakesh K. Bansal. Robust Algorithms for Detecting a Change in a Stochastic Process with Infinite Memory. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada198290.

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Siebke, Christian, Maximilian Bäumler, Madlen Ringhand, Marcus Mai, Felix Elrod, and Günther Prokop. Report on integration of the stochastic traffic simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.246.

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As part of the AutoDrive project, the OpenPASS framework is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. This framework was adapted and further developed. The deliverable D5.13 deals with the construction of the stochastic traffic simulation. At this point of the process, the theoretical design aspects of D4.20 are implemented. D5.13 explains the operating principles of the different modules. This includes the foundations, boundary conditions, and mathematical theory of the traffic simulation.
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Maes, Marc. PR-328-133600-R02 Probabilistic Corrosion Growth Models and ILI-Based Estimation Procedures - Phase II. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2015. http://dx.doi.org/10.55274/r0010842.

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The overall objective of Phase II covered by the present report, has been to implement the preferred Phase I stochastic metal loss analysis model in an effective self-standing Excel tool for matched repeat ILI data: the Hierarchical Stochastic Corrosion Growth Model (HSCGM). This analysis tool can deal with up to 5 repeat inspections and up to 1,000 (hierarchical) defects. It is a deliverable of this Project and it has been satisfactory verified and tested; its main mission is to allow flawless experimentation with the practical use of consistent stochastic metal loss processes in the context of repeat ILI inspections for pipeline integrity assessment, to explore its numerous analysis options, features, and results in the context of CGRs for pipeline integrity assessment. This report provides all the details and technical features of the HSCGM analysis tool, based on the Bayesian stochastic process approach described in the Phase I report.
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Bäumler, Maximilian, Madlen Ringhand, Christian Siebke, Marcus Mai, Felix Elrod, and Günther Prokop. Report on validation of the stochastic traffic simulation (Part B). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.243.

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This document is intended to give an overview of the validation of the human subject study, conducted in the driving simulator of the Chair of Traffic and Transportation Psychology (Verkehrspsychologie – VPSY) of the Technische Universität Dresden (TUD), as well of the validation of the stochastic traffic simulation developed in the AutoDrive project by the Chair of Automotive Engineering (Lehrstuhl Kraftfahrzeugtechnik – LKT) of TUD. Furthermore, the evaluation process of a C-AEB (Cooperative-Automatic Emergency Brake) system is demonstrated. The main purpose was to compare the driving behaviour of the study participants and the driving behaviour of the agents in the traffic simulation with real world data. Based on relevant literature, a validation concept was designed and real world data was collected using drones and stationary cameras. By means of qualitative and quantitative analysis it could be shown, that the driving simulator study shows realistic driving behaviour in terms of mean speed. Moreover, the stochastic traffic simulation already reflects reality in terms of mean and maximum speed of the agents. Finally, the performed evaluation proofed the suitability of the developed stochastic simulation for the assessment process. Furthermore, it could be shown, that a C-AEB system improves the traffic safety for the chosen test-scenarios.
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