Relevant bibliographies by topics / Stochastic modelling

Contents

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

Academic literature on the topic 'Stochastic modelling'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 July 2024

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Journal articles on the topic "Stochastic modelling"

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Varetsky, Y., and Z. Hanzelka. "STOCHASTIC MODELLING OF A HYBRID RENEWABLE ENERGY SYSTEM." Tekhnichna Elektrodynamika 2016, no. 2 (March 10, 2016): 58–62. http://dx.doi.org/10.15407/techned2016.02.058.

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Dobrow, Robert P. "Applied Stochastic Modelling." Technometrics 44, no. 1 (February 2002): 91. http://dx.doi.org/10.1198/tech.2002.s667.

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Albert, Jim. "Applied Stochastic Modelling." Journal of the American Statistical Association 97, no. 457 (March 2002): 354–55. http://dx.doi.org/10.1198/jasa.2002.s448.

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Auton, Tim. "Applied Stochastic Modelling." Journal of the Royal Statistical Society: Series D (The Statistician) 52, no. 2 (July 2003): 244. http://dx.doi.org/10.1111/1467-9884.t01-2-00356.

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Hartley, R., M. H. A. Davies, and R. B. Vintner. "Stochastic Modelling and Control." Journal of the Operational Research Society 37, no. 9 (September 1986): 928. http://dx.doi.org/10.2307/2582813.

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Blokker, Mirjam. "Stochastic Water Demand Modelling." Water Intelligence Online 10 (2011): 9781780400853. http://dx.doi.org/10.2166/9781780400853.

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Stemler, Thomas, Johannes P. Werner, Hartmut Benner, and Wolfram Just. "Stochastic modelling of intermittency." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1910 (January 13, 2010): 273–84. http://dx.doi.org/10.1098/rsta.2009.0196.

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Recently, methods have been developed to model low-dimensional chaotic systems in terms of stochastic differential equations. We tested such methods in an electronic circuit experiment. We aimed to obtain reliable drift and diffusion coefficients even without a pronounced time-scale separation of the chaotic dynamics. By comparing the analytical solutions of the corresponding Fokker–Planck equation with experimental data, we show here that crisis-induced intermittency can be described in terms of a stochastic model which is dominated by state-space-dependent diffusion. Further on, we demonstrate and discuss some limits of these modelling approaches using numerical simulations. This enables us to state a criterion that can be used to decide whether a stochastic model will capture the essential features of a given time series.
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Hartley, R. "Stochastic Modelling and Control." Journal of the Operational Research Society 37, no. 9 (November 1986): 928–29. http://dx.doi.org/10.1057/jors.1986.158.

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Jones, P. W. "Stochastic Modelling and Analysis." Technometrics 30, no. 3 (August 1988): 361. http://dx.doi.org/10.1080/00401706.1988.10488425.

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Cui, Lirong, and Haitao Liao. "Stochastic modelling with applications." IMA Journal of Management Mathematics 32, no. 1 (September 7, 2020): 1–2. http://dx.doi.org/10.1093/imaman/dpaa018.

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Dissertations / Theses on the topic "Stochastic modelling"

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Ozkan, Erhun. "Stochastic Inventory Modelling." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12612097/index.pdf.

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In this master thesis study, new inventory control mechanisms are developed for the repairables in Nedtrain. There is a multi-item, multi echelon system with a continuous review and one for one replenishment policy and there are different demand supply options in each control mechanism. There is an aggregate mean waiting time constraint in each local warehouse and the objective is to minimize the total system cost. The base stock levels in each warehouse are determined with an approximation method. Then different demand supply options are compared with each other.
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Baduraliya, Chaminda Hasitha. "Stochastic modelling in finance." Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=16938.

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The trading of financial derivatives and products in financial markets has influenced the development of the world economy. Over the last few decades, a rapid growth in complex financial systems, which can generate unstable conditions in financial markets, has been observed. Therefore models are being developed to study and examine the uncertainty surrounding these financial systems in different circumstances. The important milestone of this work can be traced to the Black-Scholes formula for option pricing which was published in 1973 and revolutionized the financial industry by introducing the no-arbitrage principle [8]. This model assumed that the average rates of return and volatility are constant, however, this is not realistic. Therefore, several models have been developed, based on pragmatic studies, which generalize the Black-Scholes formula to acquire more knowledge for these financial systems. In this project, we will focus on Stochastic Differential Equations (SDEs) models in finance which do not have explicit solutions so far. In particular, Lewis [47] developed the mean-reverting-theta processes which can not only model the volatility but also the asset price. Therefore, we will establish the Euler-Maruyama (EM) numerical schemes to approximate the solution to this model and show that the EM approximate solution will converge in probability to the true solution under certain conditions. The convergence property of the corresponding step process will be examined under the same conditions to determine its application in finance. In addition, the Markov-switching format of this model can be used to explain some erratic situations observed in financial data. Under the same conditions on parameters of mean-reverting-theta model, the Markov-switching model will be examined to show that the EM approximate solution to this model will converge in probability to the true solution. Although previous models fit to a certain type of financial data, they can not be used to explain behaviour of the unpredictable abrupt structural changes in financial markets. However, the mean-reverting-theta stochastic volatility model driven by a Poisson jump process explains some of this phenomenon. Therefore, we will examine the analytical properties of EM approximate solutions to this model for two conditions of the parameters theta and beta. Since it is possible to obtain a more generalized formula for this stochastic volatility jump process, by incorporating a hybrid concept into this SDE model, we will consider the mean-reverting-theta volatility model with Poisson jumps driven by two independent Markov processes. Existing financial instruments are not strong enough to examine the convergence property of the approximate solution to this model. Therefore, we will establish EM approximate solutions to this model and examine their convergence property, when we assume similar parameter conditions to the mean-reverting-theta model. Finally, we will show that these approximate solutions of the SDE models can be used to evaluate financial quantities, options and bonds for example.
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Chen, Peng. "Modelling the Stochastic Correlation." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188501.

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In this thesis, we mainly study the correlation between stocks. The correlation between stocks has been receiving increasing attention. Usually the correlation is considered to be a constant, although it is observed to be varying over time. In this thesis, we study the properties of correlations between Wiener processes and introduce a stochastic correlation model. Following the calibration methods by Zetocha, we implement the calibration for a new set of market data.
I det här examensarbetet fokuserar vi främst på att studera korrelation mellan aktier. Korrelationen mellan aktier har fått allt större uppmärksamhet. Vanligtvis antas korrelation vara konstant, trots att empiriska studier antyder att den är tidsvarierande. I det här examensarbetet studerar vi egenskaper hos korrelationen mellan Wienerprocesser och inför en stokastisk korrelationsmodell. Baserat på kalibreringsmetoder av Zetocha implementerar vi kalibrering för en ny uppsättning av marknadsdata.
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Lopes, Moreira de Veiga Maria Helena. "Modelling and forecasting stochastic volatility." Doctoral thesis, Universitat Autònoma de Barcelona, 2004. http://hdl.handle.net/10803/4046.

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El objetivo de esta tesis es modelar y predecir la volatilidad de las series financieras con modelos de volatilidad en tiempo discreto y continuo.
En mi primer capítulo, intento modelar las principales características de las series financieras, como a persistencia y curtosis. Los modelos de volatilidad estocástica estimados son extensiones directas de los modelos de Gallant y Tauchen (2001), donde incluyo un elemento de retro-alimentación. Este elemento es de extrema importancia porque permite captar el hecho de que períodos de alta volatilidad están, en general, seguidos de periodos de gran volatilidad y viceversa. En este capítulo, como en toda la tesis, uso el método de estimación eficiente de momentos de Gallant y Tauchen (1996). De la estimación surgen dos modelos posibles de describir los datos, el modelo logarítmico con factor de volatilidad y retroalimentación y el modelo logarítmico con dos factores de volatilidad. Como no es posible elegir entre ellos basados en los tests efectuados en la fase de la estimación, tendremos que usar el método de reprogección para obtener mas herramientas de comparación. El modelo con un factor de volatilidad se comporta muy bien y es capaz de captar la "quiebra" de los mercados financieros de 1987.
En el segundo capítulo, hago la evaluación del modelo con dos factores de volatilidad en términos de predicción y comparo esa predicción con las obtenidas con los modelos GARCH y ARFIMA. La evaluación de la predicción para los tres modelos es hecha con la ayuda del R2 de las regresiones individuales de la volatilidad "realizada" en una constante y en las predicciones. Los resultados empíricos indican un mejor comportamiento del modelo en tiempo continuo. Es más, los modelos GARCH y ARFIMA parecen tener problemas en seguir la marcha de la volatilidad "realizada".
Finalmente, en el tercer capítulo hago una extensión del modelo de volatilidad estocástica de memoria larga de Harvey (2003). O sea, introduzco un factor de volatilidad de corto plazo. Este factor extra aumenta la curtosis y ayuda a captar la persistencia (que es captada con un proceso integrado fraccional, como en Harvey (1993)). Los resultados son probados y el modelo implementado empíricamente.
The purpose of my thesis is to model and forecast the volatility of the financial series of returns by using both continuous and discrete time stochastic volatility models.
In my first chapter I try to fit the main characteristics of the financial series of returns such as: volatility persistence, volatility clustering and fat tails of the distribution of the returns.The estimated logarithmic stochastic volatility models are direct extensions of the Gallant and Tauchen's (2001) by including the feedback feature. This feature is of extreme importance because it allows to capture the low variability of the volatility factor when the factor is itself low (volatility clustering) and it also captures the increase in volatility persistence that occurs when there is an apparent change in the pattern of volatility at the very end of the sample. In this chapter, as well as in all the thesis, I use Efficient Method of Moments of Gallant and Tauchen (1996) as an estimation method. From the estimation step, two models come out, the logarithmic model with one factor of volatility and feedback (L1F) and the logarithmic model with two factors of volatility (L2). Since it is not possible to choose between them based on the diagnostics computed at the estimation step, I use the reprojection step to obtain more tools for comparing models. The L1F is able to reproject volatility quite well without even missing the crash of 1987.
In the second chapter I fit the continuous time model with two factors of volatility of Gallant and Tauchen (2001) for the return of a Microsoft share. The aim of this chapter is to evaluate the volatility forecasting performance of the continuous time stochastic volatility model comparatively to the ones obtained with the traditional GARCH and ARFIMA models. In order to inquire into this, I estimate using the Efficient Method of Moments (EMM) of Gallant and Tauchen (1996) a continuous time stochastic volatility model for the logarithm of asset price and I filter the underlying volatility using the reprojection technique of Gallant and Tauchen (1998). Under the assumption that the model is correctly specified, I obtain a consistent estimator of the integrated volatility by fitting a continuous time stochastic volatility model to the data. The forecasting evaluation for the three estimated models is going to be done with the help of the R2 of the individual regressions of realized volatility on the volatility forecasts obtained from the estimated models. The empirical results indicate the better performance of the continuous time model in the out-of-sample periods compared to the ones of the traditional GARCH and ARFIMA models. Further, these two last models show difficulties in tracking the growth pattern of the realized volatility. This probably is due to the change of pattern in volatility in this last part of the sample.
Finally, in the third chapter I come back to the model specification and I extend the long memory stochastic volatility model of Harvey (1993) by introducing a short run volatility factor. This extra factor increases kurtosis and helps the model capturing volatility persistence (that it is captured by a fractionally integrated process as in Harvey (1993) ). Futhermore, considering some restrictions of the parameters it is possible to fit the empirical fact of small first order autocorrelation of squared returns. All these results are proved theoretically and the model is implemented empirically using the S&P 500 composite index returns. The empirical results show the superiority of the model in fitting the main empirical facts of the financial series of returns.
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Löfdahl, Björn. "Stochastic modelling in disability insurance." Licentiate thesis, KTH, Matematisk statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-134233.

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This thesis consists of two papers related to the stochastic modellingof disability insurance. In the first paper, we propose a stochastic semi-Markovian framework for disability modelling in a multi-period discrete-time setting. The logistic transforms of disability inception and recovery probabilities are modelled by means of stochastic risk factors and basis functions, using counting processes and generalized linear models. The model for disability inception also takes IBNR claims into consideration. We fit various versions of the models into Swedish disability claims data. In the second paper, we consider a large, homogeneous portfolio oflife or disability annuity policies. The policies are assumed to be independent conditional on an external stochastic process representing the economic environment. Using a conditional law of large numbers, we establish the connection between risk aggregation and claims reserving for large portfolios. Further, we derive a partial differential equation for moments of present values. Moreover, we show how statistical multi-factor intensity models can be approximated by one-factor models, which allows for solving the PDEs very efficiently. Finally, we givea numerical example where moments of present values of disabilityannuities are computed using finite difference methods.

QC 20131204

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Currie, James. "Stochastic modelling of TCR binding." Thesis, University of Leeds, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590430.

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A fundamental process in the immune response to infection is the activation of T cells following contact with antigen presenting cells. This activation occurs after T cell receptors on the surface of T cells bind to immunogenic peptides expressed on the surface of antigen presenting cells. The binding of T cell receptors to ligands not only leads to the activation of T cells, it is also key to T cell selection in the thymus and the maintenance of a diverse T cell receptor repertoire. T cell receptor bindings are converted into a signal which activates a T cell but there is no universal theory which governs this process. There is experimental evidence to suggest that receptor-ligand bindings must be sufficiently long to elicit a T cell response. and that counting devices in the T cell work to allow signal accumulation, decoding and translation into biological responses. In view of these results, this thesis uses mathematical models to explore the timescales associated with T cell responses. A stochastic criterion that T cell responses occur after N receptor-ligand complexes have been bound for at least a dwell time, T, each, is used. The first model of receptor-ligand binding, in conjunction with the stochastic criterion, supports the affinity threshold hypothesis for thymic selection and agrees with the experimentally established ligand hierarchy for thymic negative selection. The initial model of ligand-receptor binding is then extended to include feedback responses, bivalent receptor binding and ligand diffusion through the immunological synapse. By including these mechanisms, the models agree with an array of experimental hypotheses which include: T cells exhibit a digital response to ligand. bivalent T cell receptor engagement stabilises receptor-ligand bindings and one ligand is sufficient to elicit a T cell response.
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Tsang, Wai-yin, and 曾慧賢. "Aspects of modelling stochastic volatility." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31223515.

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Ferreira, Jose Antonio de Sousa Jorge. "Some contributions to stochastic modelling." Thesis, University of Sheffield, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312790.

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Luo, Yang. "Stochastic modelling in biological systems." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610145.

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Dalton, Rowan. "Modelling stochastic multi-curve basis." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27102.

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As a consequence of the 2007 financial crisis, the market has shifted towards a multi-curve approach in modelling the prevailing interest rate environment. Currently, there is a reliance on the assumption of deterministic- or constant-basis spreads. This assumption is too simplistic to describe the modern multi-curve environment and serves as the motivation for this work. A stochastic-basis framework, presented by Mercurio and Xie (2012), with one- and two-factor OIS short-rate models is reviewed and implemented in order to analyse the effect of the inclusion of stochastic-basis in the pricing of interest rate derivatives. In order to preclude the existence of negative spreads in the model, a constraint on the spread model parameters is necessary. The inclusion of stochastic-basis results in a clear shift in the terminal distributions of FRA and swap rates. In spite of this, stochastic-basis is found to have a negligible effect on cap/floor and swaption prices for the admissible spread model parameters. To overcome challenges surrounding parameter estimation under the framework, a rudimentary calibration procedure is developed, where the spread model parameters are estimated from historical data; and the OIS rate model parameters are calibrated to a market swaption volatility surface.
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Books on the topic "Stochastic modelling"

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Luo, Xiaoguang. GPS Stochastic Modelling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34836-5.

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Morgan, Byron J. T. Applied stochastic modelling. 2nd ed. Boca Raton: Chapman & Hall/CRC, 2008.

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Davis, M. H. A., and R. B. Vinter. Stochastic Modelling and Control. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-4828-0.

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Germani, Alfredo, ed. Stochastic Modelling and Filtering. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/bfb0009045.

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India) International Conference on Stochastic Modelling (2002 Cochin. Advances in stochastic modelling. Edited by Artalejo J. R, Krishnamoorthy A, and International Workshop on Retrial Queues (4th : 2002 : Cochin, India). Neshanic Station, NJ: Notable Publications, 2002.

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B, Vinter R., ed. Stochastic modelling and control. London: Chapman and Hall, 1985.

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Wilkinson, Darren James. Stochastic modelling for systems biology. 2nd ed. Boca Raton: Taylor & Francis, 2012.

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Christer, Anthony H., Shunji Osaki, and Lyn C. Thomas, eds. Stochastic Modelling in Innovative Manufacturing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59105-1.

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Adler, Robert J., Peter Müller, and Boris L. Rozovskii, eds. Stochastic Modelling in Physical Oceanography. Boston, MA: Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-1-4612-2430-3.

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Hübl, Alexander. Stochastic Modelling in Production Planning. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-19120-7.

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Book chapters on the topic "Stochastic modelling"

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Ghosh, Anindya, Bapi Saha, and Prithwiraj Mal. "Stochastic Modelling." In Textile Engineering, 411–32. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003081234-12.

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Serovajsky, Simon. "Stochastic models." In Mathematical Modelling, 339–60. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003035602-18.

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Haas, Peter J. "Modelling Power." In Stochastic Petri Nets, 111–43. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/0-387-21552-2_4.

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Teng, Long, Matthias Ehrhardt, and Michael Günther. "Modelling Stochastic Correlation." In Mathematics in Industry, 113–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23413-7_14.

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Lindenschmidt, Karl-Erich. "Stochastic Modelling Framework." In River Ice Processes and Ice Flood Forecasting, 175–228. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28679-8_8.

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Lindenschmidt, Karl-Erich. "Stochastic Modelling Framework." In River Ice Processes and Ice Flood Forecasting, 195–252. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-49088-0_8.

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Davis, M. H. A., and R. B. Vinter. "Stochastic models." In Stochastic Modelling and Control, 60–99. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-4828-0_2.

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Renard, Philippe, Andres Alcolea, and David Ginsbourger. "Stochastic versus Deterministic Approaches." In Environmental Modelling, 133–49. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118351475.ch8.

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Luo, Xiaoguang. "Introduction." In GPS Stochastic Modelling, 1–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34836-5_1.

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Luo, Xiaoguang. "Mathematical Background." In GPS Stochastic Modelling, 7–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34836-5_2.

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Conference papers on the topic "Stochastic modelling"

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Ney, Hermann. "Stochastic modelling." In the workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2001. http://dx.doi.org/10.3115/1118037.1118042.

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Garbaczewski, Piotr. "Stochastic modelling of nonlinear dynamical systems." In Stochastic and chaotic dynamics in the lakes. AIP, 2000. http://dx.doi.org/10.1063/1.1302402.

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Tautu, Petre. "Stochastic Modelling in Biology." In Proceedings of the Workshop. WORLD SCIENTIFIC, 1990. http://dx.doi.org/10.1142/9789814540711.

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Maher, Mike. "Stochastic Modelling of Sport." In 2012 Ninth International Conference on Quantitative Evaluation of Systems (QEST). IEEE, 2012. http://dx.doi.org/10.1109/qest.2012.40.

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Seddon, Keith, Behnam Pirouz, and Timothy Fitton. "Stochastic beach profile modelling." In 18th International Seminar on Paste and Thickened Tailings. Australian Centre for Geomechanics, Perth, 2015. http://dx.doi.org/10.36487/acg_rep/1504_35_seddon.

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MARIANI, L., G. TURCHETTI, and F. LUCIANI. "STOCHASTIC MODELS OF IMMUNE SYSTEM AGING." In Modelling Biomedical Signals. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812778055_0007.

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Wilczyński, Bartek. "A stochastic extension of R. Thomas regulatory network modelling." In Stochastic Models in Biological Sciences. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-19.

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CH. IVANOV, PLAMEN, and CHUNG-CHUAN LO. "STOCHASTIC APPROACHES TO MODELING OF PHYSIOLOGICAL RHYTHMS." In Modelling Biomedical Signals. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812778055_0003.

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"STOCHASTIC MODELLING IN HEALTHCARE SYSTEMS." In 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003576101090115.

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Smieja, Jaroslaw. "Deterministic Modeling of Stochastic Gene Transcription Processes." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2014. http://dx.doi.org/10.2316/p.2014.809-018.

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Reports on the topic "Stochastic modelling"

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Yanev, Nikolay M., Vessela K. Stoimenova, and Dimitar V. Atanasov. Stochastic Modelling and Estimation of COVID-19 Population Dynamics. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, May 2020. http://dx.doi.org/10.7546/crabs.2020.04.02.

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Ng, B. Survey of Bayesian Models for Modelling of Stochastic Temporal Processes. Office of Scientific and Technical Information (OSTI), October 2006. http://dx.doi.org/10.2172/900168.

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Benoit, N., D. Marcotte, J. W. Molson, A F Bajc, and H. A. J. Russell. Stochastic hydrogeological modelling workflow in a glacial sedimentary basin, southern Ontario. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2020. http://dx.doi.org/10.4095/321107.

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Fleming, Wendell H., and Harold J. Kushner. Numerical Methods and Approximation and Modelling Problems in Stochastic Control Theory. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada218419.

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Osadetz, K. G., Z. Chen, and H. Gao. SuperSD, Version 1.0: a pool-based stochastic simulation program for modelling the spatial distribution of undiscovered petroleum resources. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/214036.

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Ansari, S. M., E. M. Schetselaar, and J. A. Craven. Three-dimensional magnetotelluric modelling of the Lalor volcanogenic massive-sulfide deposit, Manitoba. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/328003.

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Unconstrained magnetotelluric inversion commonly produces insufficient inherent resolution to image ore-system fluid pathways that were structurally thinned during post-emplacement tectonic activity. To improve the resolution in these complex environments, we synthesized the 3-D magnetotelluric (MT) response for geologically realistic models using a finite-element-based forward-modelling tool with unstructured meshes and applied it to the Lalor volcanogenic massive-sulfide deposit in the Snow Lake mining camp, Manitoba. This new tool is based on mapping interpolated or simulated resistivity values from wireline logs onto unstructured tetrahedral meshes to reflect, with the help of 3-D models obtained from lithostratigraphic and lithofacies drillhole logs, the complexity of the host-rock geological structure. The resulting stochastic model provides a more realistic representation of the heterogeneous spatial distribution of the electric resistivity values around the massive, stringer, and disseminated sulfide ore zones. Both models were combined into one seamless tetrahedral mesh of the resistivity field. To capture the complex resistivity distribution in the geophysical forward model, a finite-element code was developed. Comparative analyses of the forward models with MT data acquired at the Earth's surface show a reasonable agreement that explains the regional variations associated with the host rock geological structure and detects the local anomalies associated with the MT response of the ore zones.
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Perdigão, Rui A. P., and Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, November 2020. http://dx.doi.org/10.46337/201111.

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Causality and Predictability of Complex Systems pose fundamental challenges even under well-defined structural stochastic-dynamic conditions where the laws of motion and system symmetries are known. However, the edifice of complexity can be profoundly transformed by structural-functional coevolution and non-recurrent elusive mechanisms changing the very same invariants of motion that had been taken for granted. This leads to recurrence collapse and memory loss, precluding the ability of traditional stochastic-dynamic and information-theoretic metrics to provide reliable information about the non-recurrent emergence of fundamental new properties absent from the a priori kinematic geometric and statistical features. Unveiling causal mechanisms and eliciting system dynamic predictability under such challenging conditions is not only a fundamental problem in mathematical and statistical physics, but also one of critical importance to dynamic modelling, risk assessment and decision support e.g. regarding non-recurrent critical transitions and extreme events. In order to address these challenges, generalized metrics in non-ergodic information physics are hereby introduced for unveiling elusive dynamics, causality and predictability of complex dynamical systems undergoing far-from-equilibrium structural-functional coevolution. With these methodological developments at hand, hidden dynamic information is hereby brought out and explicitly quantified even beyond post-critical regime collapse, long after statistical information is lost. The added causal insights and operational predictive value are further highlighted by evaluating the new information metrics among statistically independent variables, where traditional techniques therefore find no information links. Notwithstanding the factorability of the distributions associated to the aforementioned independent variables, synergistic and redundant information are found to emerge from microphysical, event-scale codependencies in far-from-equilibrium nonlinear statistical mechanics. The findings are illustrated to shed light onto fundamental causal mechanisms and unveil elusive dynamic predictability of non-recurrent critical transitions and extreme events across multiscale hydro-climatic problems.
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Perdigão, Rui A. P. New Horizons of Predictability in Complex Dynamical Systems: From Fundamental Physics to Climate and Society. Meteoceanics, October 2021. http://dx.doi.org/10.46337/211021.

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Discerning the dynamics of complex systems in a mathematically rigorous and physically consistent manner is as fascinating as intimidating of a challenge, stirring deeply and intrinsically with the most fundamental Physics, while at the same time percolating through the deepest meanders of quotidian life. The socio-natural coevolution in climate dynamics is an example of that, exhibiting a striking articulation between governing principles and free will, in a stochastic-dynamic resonance that goes way beyond a reductionist dichotomy between cosmos and chaos. Subjacent to the conceptual and operational interdisciplinarity of that challenge, lies the simple formal elegance of a lingua franca for communication with Nature. This emerges from the innermost mathematical core of the Physics of Coevolutionary Complex Systems, articulating the wealth of insights and flavours from frontier natural, social and technical sciences in a coherent, integrated manner. Communicating thus with Nature, we equip ourselves with formal tools to better appreciate and discern complexity, by deciphering a synergistic codex underlying its emergence and dynamics. Thereby opening new pathways to see the “invisible” and predict the “unpredictable” – including relative to emergent non-recurrent phenomena such as irreversible transformations and extreme geophysical events in a changing climate. Frontier advances will be shared pertaining a dynamic that translates not only the formal, aesthetical and functional beauty of the Physics of Coevolutionary Complex Systems, but also enables and capacitates the analysis, modelling and decision support in crucial matters for the environment and society. By taking our emerging Physics in an optic of operational empowerment, some of our pioneering advances will be addressed such as the intelligence system Earth System Dynamic Intelligence and the Meteoceanics QITES Constellation, at the interface between frontier non-linear dynamics and emerging quantum technologies, to take the pulse of our planet, including in the detection and early warning of extreme geophysical events from Space.
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