Relevant bibliographies by topics / Stochastic dynamics

Academic literature on the topic 'Stochastic dynamics'

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Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Stochastic dynamics"

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IMKELLER, PETER, and ADAM HUGH MONAHAN. "CONCEPTUAL STOCHASTIC CLIMATE MODELS." Stochastics and Dynamics 02, no. 03 (September 2002): 311–26. http://dx.doi.org/10.1142/s0219493702000443.

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From July 9 to 11, 2001, 50 researchers from the fields of climate dynamics and stochastic analysis met in Chorin, Germany, to discuss the idea of stochastic models of climate. The present issue of Stochastics and Dynamics collects several papers from this meeting. In this introduction to the volume, the idea of simple conceptual stochastic climate models is introduced amd recent results in the mathematically rigorous development and analysis of such models are reviewed. As well, a brief overview of the application of ideas from stochastic dynamics to simple models of the climate system is given.
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Ferrandis, Eduardo. "On the stochastic approach to marine population dynamics." Scientia Marina 71, no. 1 (March 30, 2007): 153–74. http://dx.doi.org/10.3989/scimar.2007.71n1153.

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Manolis, George D. "Stochastic soil dynamics." Soil Dynamics and Earthquake Engineering 22, no. 1 (January 2002): 3–15. http://dx.doi.org/10.1016/s0267-7261(01)00055-0.

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Cabrales, Antonio. "Stochastic Replicator Dynamics." International Economic Review 41, no. 2 (May 2000): 451–81. http://dx.doi.org/10.1111/1468-2354.00071.

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Mantelli, Elisa, Matteo Bernard Bertagni, and Luca Ridolfi. "Stochastic ice stream dynamics." Proceedings of the National Academy of Sciences 113, no. 32 (July 25, 2016): E4594—E4600. http://dx.doi.org/10.1073/pnas.1600362113.

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Ice streams are narrow corridors of fast-flowing ice that constitute the arterial drainage network of ice sheets. Therefore, changes in ice stream flow are key to understanding paleoclimate, sea level changes, and rapid disintegration of ice sheets during deglaciation. The dynamics of ice flow are tightly coupled to the climate system through atmospheric temperature and snow recharge, which are known exhibit stochastic variability. Here we focus on the interplay between stochastic climate forcing and ice stream temporal dynamics. Our work demonstrates that realistic climate fluctuations are able to (i) induce the coexistence of dynamic behaviors that would be incompatible in a purely deterministic system and (ii) drive ice stream flow away from the regime expected in a steady climate. We conclude that environmental noise appears to be crucial to interpreting the past behavior of ice sheets, as well as to predicting their future evolution.
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Ohzeki, Masayuki. "Stochastic gradient method with accelerated stochastic dynamics." Journal of Physics: Conference Series 699 (March 2016): 012019. http://dx.doi.org/10.1088/1742-6596/699/1/012019.

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Sopasakis, Alexandros. "Lattice Free Stochastic Dynamics." Communications in Computational Physics 12, no. 3 (September 2012): 691–702. http://dx.doi.org/10.4208/cicp.110211.200611a.

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AbstractWe introduce a lattice-free hard sphere exclusion stochastic process. The resulting stochastic rates are distance based instead of cell based. The corresponding Markov chain build for this many particle system is updated using an adaptation of the kinetic Monte Carlo method. It becomes quickly apparent that due to the lattice-free environment, and because of that alone, the dynamics behave differently than those in the lattice-based environment. This difference becomes increasingly larger with respect to particle densities/temperatures. The well-known packing problem and its solution (Palasti conjecture) seem to validate the resulting lattice-free dynamics.
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Landim, Claudio, Stefano Olla, and Herbert Spohn. "Large Scale Stochastic Dynamics." Oberwolfach Reports 10, no. 4 (2013): 3039–113. http://dx.doi.org/10.4171/owr/2013/52.

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Bodineau, Thierry, Fabio Toninelli, and Bálint Tóth. "Large Scale Stochastic Dynamics." Oberwolfach Reports 13, no. 4 (December 20, 2017): 3031–86. http://dx.doi.org/10.4171/owr/2016/54.

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Bodineau, Thierry, Fabio Toninelli, and Bálint Tóth. "Large Scale Stochastic Dynamics." Oberwolfach Reports 16, no. 3 (September 9, 2020): 2605–69. http://dx.doi.org/10.4171/owr/2019/42.

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Dissertations / Theses on the topic "Stochastic dynamics"

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Dean, David Stanley. "Stochastic dynamics." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318048.

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Lythe, Grant David. "Stochastic slow-fast dynamics." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338108.

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Restrepo, Juan M., and Shankar Venkataramani. "Stochastic longshore current dynamics." ELSEVIER SCI LTD, 2016. http://hdl.handle.net/10150/621938.

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We develop a stochastic parametrization, based on a 'simple' deterministic model for the dynamics of steady longshore currents, that produces ensembles that are statistically consistent with field observations of these currents. Unlike deterministic models, stochastic parameterization incorporates randomness and hence can only match the observations in a statistical sense. Unlike statistical emulators, in which the model is tuned to the statistical structure of the observation, stochastic parametrization are not directly tuned to match the statistics of the observations. Rather, stochastic parameterization combines deterministic, i.e physics based models with stochastic models for the "missing physics" to create hybrid models, that are stochastic, but yet can be used for making predictions, especially in the context of data assimilation. We introduce a novel measure of the utility of stochastic models of complex processes, that we call consistency of sensitivity. A model with poor consistency of sensitivity requires a great deal of tuning of parameters and has a very narrow range of realistic parameters leading to outcomes consistent with a reasonable spectrum of physical outcomes. We apply this metric to our stochastic parametrization and show that, the loss of certainty inherent in model due to its stochastic nature is offset by the model's resulting consistency of sensitivity. In particular, the stochastic model still retains the forward sensitivity of the deterministic model and hence respects important structural/physical constraints, yet has a broader range of parameters capable of producing outcomes consistent with the field data used in evaluating the model. This leads to an expanded range of model applicability. We show, in the context of data assimilation, the stochastic parametrization of longshore currents achieves good results in capturing the statistics of observation that were not used in tuning the model.
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Yilmaz, Bulent. "Stochastic Approach To Fusion Dynamics." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608517/index.pdf.

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This doctoral study consists of two parts. In the first part, the quantum statistical effects on the formation process of the heavy ion fusion reactions have been investigated by using the c-number quantum Langevin equation approach. It has been shown that the quantum effects enhance the over-passing probability at low temperatures. In the second part, we have developed a simulation technique for the quantum noises which can be approximated by two-term exponential colored noise.
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De, Fabritiis Gianni. "Stochastic dynamics of mesoscopic fluids." Thesis, Queen Mary, University of London, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268402.

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Stocks, Nigel Geoffrey. "Experiments in stochastic nonlinear dynamics." Thesis, Lancaster University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315224.

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Xie, Yan. "STOCHASTIC DYNAMICS OF GENE TRANSCRIPTION." UKnowledge, 2011. http://uknowledge.uky.edu/statistics_etds/2.

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Gene transcription in individual living cells is inevitably a stochastic and dynamic process. Little is known about how cells and organisms learn to balance the fidelity of transcriptional control and the stochasticity of transcription dynamics. In an effort to elucidate the contribution of environmental signals to this intricate balance, a Three State Model was recently proposed, and the transcription system was assumed to transit among three different functional states randomly. In this work, we employ this model to demonstrate how the stochastic dynamics of gene transcription can be characterized by the three transition parameters. We compute the probability distribution of a zero transcript event and its conjugate, the distribution of the time durations in gene on or gene off periods, the transition frequency between system states, and the transcriptional bursting frequency. We also exemplify the mathematical results by the experimental data on prokaryotic and eukaryotic transcription. The analysis reveals that no promoters will be definitely turned on to transcribe within a finite time period, no matter how strong the induction signals are applied, and how abundant the activators are available. Although stronger extrinsic signals could enhance promoter activation rate, the promoter creates an intrinsic ceiling that no signals could cross over in a finite time. Consequently, among a large population of isogenic cells, only a portion of the cells, but not the whole population, could be induced by environmental signals to express a particular gene within a finite time period. We prove that the gene on duration follows an exponential distribution, and the gene off intervals show a local maximum that is best described by assuming two sequential exponential process. The transition frequencies are determined by a system of stochastic differential equations, or equivalently, an iterative scheme of integral operators. We prove that for each positive integer n , there associates a unique time, called the peak instant, at which the nth transcript synthesis cycle since time zero proceeds most likely. These moments constitute a time series preserving the nature order of n.
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Naylor, Sarah Louise. "Stochastic dynamics in periodic potentials." Thesis, University of Nottingham, 2006. http://eprints.nottingham.ac.uk/10179/.

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This thesis describes the dynamics of both electrons and atoms in periodic potentials. In particular, it explores how such potentials can be used to realise a new type of quantum chaos in which the effective classical Hamiltonian originates from the intrinsically quantum nature of energy bands. Firstly, this study examines electron dynamics in a superlattice with an applied voltage and a tilted magnetic field. This system displays a rare type of chaos known as non-KAM (Kolmogorov-Arnold-Moser) chaos, which switches on abruptly when an applied perturbation reaches certain critical values. The onset of chaos in the system leads to the formation of complex patterns in phase space known as stochastic webs. The electron behaviour under these conditions is analysed both semiclassically and quantum mechanically, and the results compared to experimental studies. We show that the presence of stochastic webs strongly enhances electron transport. We calculate Wigner functions of the electron wavefunction at various times and show that, when compared to the Poincare sections, evidence of stochastic web formation is observed in the quantum mechanical phase space. Two designs of superlattice are studied and we show, in a full quantum mechanical analysis, that the design of the superlattice has a pronounced effect on the probability of inter-miniband tunnelling and hence the calculated and measured transport characteristics. Secondly, we explore the dynamics of an ultra-cold sodium atom falling through an optical lattice whilst confined in a harmonic gutter potential that is tilted at an angle to the lattice axis. We show this system is analogous to the case of an electron in a superlattice, and that the atomic dynamics show similar enhanced transport properties for certain trapping frequencies. We also find that in a full quantum mechanical calculation, the atomic wavepacket tends to fragment as the angle at which the gutter potential is tilted is increased. Finally, we examine the dynamics of a Bose-Einstein condensate falling through an optical lattice whilst confined in a harmonic gutter potential. We vary the strength of the interatomic interaction parameter to investigate the role of interactions in the system and find that, even for small tilt angles, the condensate wavefunction fragments. For large interaction parameters combined with large tilt angles, the wavefunction explodes catastrophically.
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Herbert, Julian Richard. "Stochastic processes for parasite dynamics." Thesis, University College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368164.

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Griffin, T. C. L. "Dynamics of stochastic nonsmooth systems." Thesis, University of Bristol, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.411095.

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Books on the topic "Stochastic dynamics"

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1950-, Schimansky-Geier Lutz, Pöschel Thorsten 1963-, and Ebeling Werner 1936-, eds. Stochastic dynamics. Berlin: Springer, 1997.

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Schimansky-Geier, Lutz, and Thorsten Pöschel, eds. Stochastic Dynamics. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0105592.

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Crauel, Hans, and Matthias Gundlach. Stochastic Dynamics. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/b97846.

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1956-, Crauel H., and Gundlach Matthias, eds. Stochastic dynamics. New York: Springer, 1999.

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Elishakoff, I. Stochastic structural dynamics. London: Springer Verlag, 1991.

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Huang, Yu, Min Xiong, and Liuyuan Zhao. Slope Stochastic Dynamics. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9697-8.

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To, Cho W. S. Stochastic Structural Dynamics. Chichester, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118402757.

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S, Jensen Bjarne, and Palokangas Tapio, eds. Stochastic economic dynamics. [Copenhagen?]: Copenhagen Business School Press, 2007.

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Li, Jie. Stochastic dynamics of structures. Singapore: Wiley, 2009.

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El Hami, Abdelkhalak, and Bouchaib Radi. Stochastic Dynamics of Structures. Hoboken, NJ, USA: John Wiley &;#38; Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119377610.

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Book chapters on the topic "Stochastic dynamics"

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Siegelmann, Hava T. "Stochastic Dynamics." In Neural Networks and Analog Computation, 121–39. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-0707-8_9.

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Feng, Shui. "Stochastic Dynamics." In Probability and its Applications, 81–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11194-5_5.

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Cranston, Michael, and Yves Le Jan. "Asymptotic Curvature for Stochastic Dynamical Systems." In Stochastic Dynamics, 327–38. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/0-387-22655-9_14.

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van Kampen, Nico G. "Probability in physics." In Stochastic Dynamics, 1–4. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0105593.

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Van den Broeck, C. "From stratonovich calculus to noise-induced phase transitions." In Stochastic Dynamics, 6–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0105594.

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Au, Siu-Kui. "Stochastic Structural Dynamics." In Operational Modal Analysis, 179–204. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4118-1_5.

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Young, H. Peyton. "Stochastic Adaptive Dynamics." In The New Palgrave Dictionary of Economics, 1–7. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_1990-1.

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Young, H. Peyton. "Stochastic Adaptive Dynamics." In The New Palgrave Dictionary of Economics, 13089–95. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_1990.

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Durlauf, Steven N., and Lawrence E. Blume. "Stochastic Adaptive Dynamics." In Game Theory, 325–32. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280847_34.

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Tucci, Marco P. "Stochastic Sustainability." In Sustainability: Dynamics and Uncertainty, 151–69. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-4892-4_8.

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Conference papers on the topic "Stochastic dynamics"

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Zadorozhnyi, Vladimir N., Evgeniy B. Yudin, and Maria N. Yudina. "Graphs with complex stochastic increments." In 2017 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2017. http://dx.doi.org/10.1109/dynamics.2017.8239525.

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Gontchar, Igor I., Maria V. Chushnyakova, Vera K. Volkova, and Alexander I. Blesman. "Modeling a two-dimensional distorted stochastic harmonic oscillator." In 2017 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2017. http://dx.doi.org/10.1109/dynamics.2017.8239454.

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Stocks, N. G. "Suprathreshold stochastic resonance." In Stochastic and chaotic dynamics in the lakes. AIP, 2000. http://dx.doi.org/10.1063/1.1302415.

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Champagnat, Nicolas, and Amaury Lambert. "Adaptive dynamics in logistic branching populations." In Stochastic Models in Biological Sciences. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-14.

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P., Spanos, Pirrotta A., Marino F., and Robledo Ricardo L. A. "Stochastic Analysis of Motorcycle Dynamics." In 6th International Conference on Computational Stochastic Mechanics. Singapore: Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p056.

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Ortega, Guillermo J. "Detecting deterministic dynamics in stochastic systems." In Stochastic and chaotic dynamics in the lakes. AIP, 2000. http://dx.doi.org/10.1063/1.1302449.

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Barbay, Sylvain. "Experimental investigation of stochastic resonance." In Stochastic and chaotic dynamics in the lakes. AIP, 2000. http://dx.doi.org/10.1063/1.1302413.

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Hasty, Jeff. "Stochastic regulation of gene expression." In Stochastic and chaotic dynamics in the lakes. AIP, 2000. http://dx.doi.org/10.1063/1.1302384.

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Malysheva, Nadezhda N., and Aleksandr A. Pavlov. "Determination of probabilistic descriptions and stochastic processes of changes loads." In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7819045.

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Malysheva, Nadezhda N., and Aleksandr A. Pavlov. "Determination of probabilistic descriptions and stochastic processes of changes loads." In 2017 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2017. http://dx.doi.org/10.1109/dynamics.2017.8239485.

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Reports on the topic "Stochastic dynamics"

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HOLM, D. D., A. ACEVES, J. S. ALLEN, and ET AL. APPLIED NONLINEAR STOCHASTIC DYNAMICS. Office of Scientific and Technical Information (OSTI), November 1999. http://dx.doi.org/10.2172/785030.

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Leen, Todd K. Stochastic Learning Dynamics and Non-Linear Dimension Reduction. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada292818.

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Paul, Mark R. Pushing Measurement to the Ultimate Stochastic Limit: The Stochastic Dynamics of Fluid-Coupled Nanocantilevers. Fort Belvoir, VA: Defense Technical Information Center, February 2010. http://dx.doi.org/10.21236/ada515630.

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Lai, Ying-Cheng. Research on Nonlinear and Stochastic Dynamics with Defense Applications. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada498292.

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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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Alloza, Mario, Jorge Martínez, Juan Rojas, and Iacopo Varotto. Public debt dynamics: a stochastic approach applied to Spain. Madrid: Banco de España, June 2024. http://dx.doi.org/10.53479/36693.

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This paper presents a methodology for analysing public debt sustainability that incorporates factors that enable uncertainty in the macro-financial environment to be quantified. The aim is to identify risks, not only under specific assumptions, but also considering a complete characterisation of potential developments in the real economy and in financing costs, based on the historical evidence available. To this end, stochastic shocks are included in the equations for a standard debt sustainability analysis (DSA) model, using recent evidence to gauge their scale and recurrence. When applied to Spain, the results suggest that uncertainty over the macro-financial environment and the growing pressure of the costs of ageing pose a challenge for the sustainability of our public finances. Specifically, in the absence of new fiscal consolidation measures, it is estimated that the probability of public debt in Spain being above 100% of GDP in 2040 is 80%. However, in a scenario characterised by a consolidation policy consistent with the new European economic governance framework, that probability would drop to 20%.
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Melcangi, Davide, and Silvia Sarpietro. Nonlinear Firm Dynamics. Federal Reserve Bank of New York, March 2024. http://dx.doi.org/10.59576/sr.1088.

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This paper presents empirical evidence on the nature of idiosyncratic shocks to firms and discusses its role for firm behavior and aggregate fluctuations. We document that firm-level sales and productivity are hit by heavy-tailed shocks and follow a nonlinear stochastic process, thus departing from the canonical linear. We estimate a state-of-the-art model to flexibly capture the rich dynamics uncovered in the data and characterize the drivers of nonlinear persistence and non-Gaussian shocks. We show that these features are crucial to get empirically plausible volatility and persistence of micro-originated (granular) aggregate fluctuations.
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Roemer, Peter A. Stochastic Modeling of the Persistence of HIV: Early Population Dynamics. Fort Belvoir, VA: Defense Technical Information Center, May 2013. http://dx.doi.org/10.21236/ada581866.

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Froot, Kenneth, and Maurice Obstfeld. Exchange Rate Dynamics Under Stochastic Regime Shifts: A Unified Approach. Cambridge, MA: National Bureau of Economic Research, February 1989. http://dx.doi.org/10.3386/w2835.

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Aliprantis, Dionissi, Daniel R. Carroll, and Eric R. Young. The Dynamics of the Racial Wealth Gap. Federal Reserve Bank of Cleveland, November 2022. http://dx.doi.org/10.26509/frbc-wp-201918r.

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What drives the dynamics of the racial wealth gap? We answer this question using a dynamic stochastic general equilibrium heterogeneous-agents model. Our calibrated model endogenously produces a racial wealth gap matching that observed in recent decades along with key features of the current cross-sectional distribution of wealth, earnings, intergenerational transfers, and race. Our model predicts that equalizing earnings is by far the most important mechanism for permanently closing the racial wealth gap. One-time wealth transfers have only transitory effects unless they address the racial earnings gap, and return gaps only matter when earnings inequality is reduced.
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