Relevant bibliographies by topics / Stochastic

Contents

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

Academic literature on the topic 'Stochastic'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Stochastic"

1

Engelbert, H. J., and V. P. Kurenok. "On Multidimensional SDEs Without Drift and with A Time-Dependent Diffusion Matrix." Georgian Mathematical Journal 7, no. 4 (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: 187–19
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Luo, Mei-Ju, and Yuan Lu. "Properties of Expected Residual Minimization Model for a Class of Stochastic Complementarity Problems." Journal of Applied Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/497586.

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Expected residual minimization (ERM) model which minimizes an expected residual function defined by an NCP function has been studied in the literature for solving stochastic complementarity problems. In this paper, we first give the definitions of stochasticP-function, stochasticP0-function, and stochastic uniformlyP-function. Furthermore, the conditions such that the function is a stochasticPP0-function are considered. We then study the boundedness of solution set and global error bounds of the expected residual functions defined by the “Fischer-Burmeister” (FB) function and “min” function. T
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IMKELLER, PETER, and ADAM HUGH MONAHAN. "CONCEPTUAL STOCHASTIC CLIMATE MODELS." Stochastics and Dynamics 02, no. 03 (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 giv
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Liu, Caixia, Yingqi Zhang, and Huixia Sun. "Finite-TimeH∞Filtering for Singular Stochastic Systems." Journal of Applied Mathematics 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/615790.

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This paper addresses the problem of finite-timeH∞filtering for one family of singular stochastic systems with parametric uncertainties and time-varying norm-bounded disturbance. Initially, the definitions of singular stochastic finite-time boundedness and singular stochasticH∞finite-time boundedness are presented. Then, theH∞filtering is designed for the class of singular stochastic systems with or without uncertain parameters to ensure singular stochastic finite-time boundedness of the filtering error system and satisfy a prescribedH∞performance level in some given finite-time interval. Furth
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LIUM, ARNT-GUNNAR, TEODOR GABRIEL CRAINIC, and STEIN W. WALLACE. "CORRELATIONS IN STOCHASTIC PROGRAMMING: A CASE FROM STOCHASTIC SERVICE NETWORK DESIGN." Asia-Pacific Journal of Operational Research 24, no. 02 (2007): 161–79. http://dx.doi.org/10.1142/s0217595907001206.

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Deterministic models, even if used repeatedly, will not capture the essence of planning in an uncertain world. Flexibility and robustness can only be properly valued in models that use stochastics explicitly, such as stochastic optimization models. However, it may also be very important to capture how the random phenomena are related to one another. In this article we show how the solution to a stochastic service network design model depends heavily on the correlation structure among the random demands. The major goal of this paper is to discuss why this happens, and to provide insights into t
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Zhang, Yingqi, Wei Cheng, Xiaowu Mu та Caixia Liu. "Stochasticℋ∞Finite-Time Control of Discrete-Time Systems with Packet Loss". Mathematical Problems in Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/897481.

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This paper investigates the stochastic finite-time stabilization andℋ∞control problem for one family of linear discrete-time systems over networks with packet loss, parametric uncertainties, and time-varying norm-bounded disturbance. Firstly, the dynamic model description studied is given, which, if the packet dropout is assumed to be a discrete-time homogenous Markov process, the class of discrete-time linear systems with packet loss can be regarded as Markovian jump systems. Based on Lyapunov function approach, sufficient conditions are established for the resulting closed-loop discrete-time
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Polyakova, A. Yu. "Feat ures of continuing formati on of stochastic cult ure of schoolchildren in the conditions of dista nce learning." Informatics in school, no. 6 (September 25, 2021): 39–48. http://dx.doi.org/10.32517/2221-1993-2021-20-6-39-48.

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The article describes the essence of continuity in the formation of a stochastic culture of students in the conditions of distance learning. The author’s definition of the concept “continuity in the formation of stochastic culture of students of a general education school in the conditions of distance learning” is given. There are the basic conditions of a teacher’s possession of the highest level of stochastic culture, due to which the formation of an integral personal quality, which is a generalized indicator of stochastic competence, takes place in schoolchildren. Modern information and com
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Sankar, T. S., S. A. Ramu, and R. Ganesan. "Stochastic Finite Element Analysis for High Speed Rotors." Journal of Vibration and Acoustics 115, no. 1 (1993): 59–64. http://dx.doi.org/10.1115/1.2930315.

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The general problem of the dynamic response of highspeed rotors is considered in which certain system parameters may have a spatial stochastic variation. In particular the elastic modulus and mass density of a rotating shaft are described through one dimensional stochastic field functions so that the imperfections in manufacture and measurement can be accounted for. The stochastic finite element method is developed so that the variability of the response of the rotor can be interpreted in terms of the variation of the material property. As an illustration the whirl speed analysis is performed
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Hu, Peng, та Chengming Huang. "The StochasticΘ-Method for Nonlinear Stochastic Volterra Integro-Differential Equations". Abstract and Applied Analysis 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/583930.

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The stochasticΘ-method is extended to solve nonlinear stochastic Volterra integro-differential equations. The mean-square convergence and asymptotic stability of the method are studied. First, we prove that the stochasticΘ-method is convergent of order1/2in mean-square sense for such equations. Then, a sufficient condition for mean-square exponential stability of the true solution is given. Under this condition, it is shown that the stochasticΘ-method is mean-square asymptotically stable for every stepsize if1/2≤θ≤1and when0≤θ<1/2, the stochasticΘ-method is mean-square asymptotically stable
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Lykova, K. G. "METHODOLOGY OF FORMING A STOCHASTIC WORLDVIEW WHILE STUDYING THE SECTION "RANDOM EVENTS. VEROBILITIES"." Educational Psychology in Polycultural Space 56, no. 4 (2021): 67–77. http://dx.doi.org/10.24888/2073-8439-2021-56-4-67-77.

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The article contains general provisions, characterizing the methodology of forming stochastic worldview of high school students. The methodology is carried out in accordance with the developed worldview significant educational material, accompanied by the support of specially selected digital technology, and in accordance with the main stages of the formation of worldview while teaching mathematics. The main stages of the implementation of the holistic process of learning stochasticity are: preparatory, problem-research, implementation and correctional. It is supposed that the methodology of f
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Dissertations / Theses on the topic "Stochastic"

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Abi, Jaber Eduardo. "Stochastic Invariance and Stochastic Volterra Equations." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLED025/document.

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La présente thèse traite de la théorie des équations stochastiques en dimension finie. Dans la première partie, nous dérivons des conditions géométriques nécessaires et suffisantes sur les coefficients d’une équation différentielle stochastique pour l’existence d’une solution contrainte à rester dans un domaine fermé, sous de faibles conditions de régularité sur les coefficients.Dans la seconde partie, nous abordons des problèmes d’existence et d’unicité d’équations de Volterra stochastiques de type convolutif. Ces équations sont en général non-Markoviennes. Nous établissons leur correspondanc
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Yang, Weiye. "Stochastic analysis and stochastic PDEs on fractals." Thesis, University of Oxford, 2018. http://ora.ox.ac.uk/objects/uuid:43a7af74-c531-424a-9f3d-4277138affbb.

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Stochastic analysis on fractals is, as one might expect, a subfield of analysis on fractals. An intuitive starting point is to observe that on many fractals, one can define diffusion processes whose law is in some sense invariant with respect to the symmetries and self-similarities of the fractal. These can be interpreted as fractal-valued counterparts of standard Brownian motion on Rd. One can study these diffusions directly, for example by computing heat kernel and hitting time estimates. On the other hand, by associating the infinitesimal generator of the fractal-valued diffusion with the L
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Fei, Lin. "On a stochastic optimization technique : stochastic probing /." The Ohio State University, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487777901661535.

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Ozkan, Pelin. "Analysis Of Stochastic And Non-stochastic Volatility Models." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605421/index.pdf.

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Changing in variance or volatility with time can be modeled as deterministic by using autoregressive conditional heteroscedastic (ARCH) type models, or as stochastic by using stochastic volatility (SV) models. This study compares these two kinds of models which are estimated on Turkish / USA exchange rate data. First, a GARCH(1,1) model is fitted to the data by using the package E-views and then a Bayesian estimation procedure is used for estimating an appropriate SV model with the help of Ox code. In order to compare these models, the LR test statistic calculated for non-nested hypotheses is
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Yakowitz, Diana Schadl. "Two-stage stochastic linear programming: Stochastic decomposition approaches." Diss., The University of Arizona, 1991. http://hdl.handle.net/10150/185342.

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Stochastic linear programming problems are linear programming problems for which one or more data elements are described by random variables. Two-stage stochastic linear programming problems are problems in which a first stage decision is made before the random variables are observed. A second stage, or recourse decision, which varies with these observations compensates for any deficiencies which result from the earlier decision. Many applications areas including water resources, industrial management, economics and finance lead to two-stage stochastic linear programs with recourse. In this di
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Kůdela, Jakub. "Advanced Decomposition Methods in Stochastic Convex Optimization." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-403864.

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Při práci s úlohami stochastického programování se často setkáváme s optimalizačními problémy, které jsou příliš rozsáhlé na to, aby byly zpracovány pomocí rutinních metod matematického programování. Nicméně, v některých případech mají tyto problémy vhodnou strukturu, umožňující použití specializovaných dekompozičních metod, které lze použít při řešení rozsáhlých optimalizačních problémů. Tato práce se zabývá dvěma třídami úloh stochastického programování, které mají speciální strukturu, a to dvoustupňovými stochastickými úlohami a úlohami s pravděpodobnostním omezením, a pokročilými dekompozi
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Andersson, Kristina. "Stochastic Volatility." Thesis, Uppsala University, Department of Mathematics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121722.

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Huang, Chueng-Chiu S. "Stochastic scheduling." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/24834.

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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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Vedin, Robert. "Stochastic Resonance." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193632.

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Noise is often viewed as something unfortunate and unavoidable, however with the development of Stochastic Resonance (SR) theory it has been shown to have beneficial effects in many non-linear systems. We have explored the SR phenomenon via numerical simulations of two such systems. The first one is a one-dimensional Brownian particle in a bi-stable potential and the second a simple model of a signal neuron both subject to a periodic input signal. We have investigated the system responses for different input signal frequencies and noise levels in order to determine both an optimal noise level
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Books on the topic "Stochastic"

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Conference on Stochastic Processes and Their Applications (33rd : 2009 : Berlin, Germany), ed. Surveys in stochastic processes. European Mathematical Society, 2011.

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Truman, Aubrey, and Ian M. Davies, eds. Stochastic Mechanics and Stochastic Processes. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/bfb0077911.

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1975-, McDonnell Mark D., ed. Stochastic resonance: From suprathreshold stochastic resonance to stochastic signal quantization. Cambridge University Press, 2008.

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Yin, George, and Thaleia Zariphopoulou, eds. Stochastic Analysis, Filtering, and Stochastic Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98519-6.

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Liu, Shu-Jun, and Miroslav Krstic. Stochastic Averaging and Stochastic Extremum Seeking. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4087-0.

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Kunita, H. Stochastic flows and stochastic differential equations. Cambridge University Press, 1990.

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Kunita, Hiroshi. Stochastic flows and stochastic differential equations. Cambridge UniversityPress, 1990.

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Liu, Shu-Jun. Stochastic Averaging and Stochastic Extremum Seeking. Springer London, 2012.

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

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Stepanov, Sergey S. Stochastic World. Springer International Publishing, 2013.

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Book chapters on the topic "Stochastic"

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Chang, Cheng-Shang, J. George Shanthikumar, and David D. Yao. "Stochastic Convexity and Stochastic Majorization." In Stochastic Modeling and Analysis of Manufacturing Systems. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2670-3_5.

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Iacus, Stefano M. "Stochastic Processes and Stochastic Differential Equations." In Springer Series in Statistics. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-75839-8_1.

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Doleans–Dade, C. "Stochastic Processes and Stochastic Differential Equations." In Stochastic Differential Equations. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11079-5_1.

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Kunita, Hiroshi. "Stochastic Differential Equations and Stochastic Flows." In Stochastic Flows and Jump-Diffusions. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3801-4_3.

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Tallón-Ballesteros, Antonio J., Luís Correia, and Sung-Bae Cho. "Stochastic and Non-Stochastic Feature Selection." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68935-7_64.

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Pekergin, Nihal, and Sana Younès. "Stochastic Model Checking with Stochastic Comparison." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11549970_9.

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Wong, Eugene, and Bruce Hajek. "Stochastic Integrals and Stochastic Differential Equations." In Springer Texts in Electrical Engineering. Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5060-9_4.

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Hazareesingh, L., and D. Kannan. "Stochastic product integration and stochastic equations." In Lecture Notes in Mathematics. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/bfb0072884.

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Stepanov, Sergey S. "Stochastic Equations." In Stochastic World. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00071-8_2.

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Stepanov, Sergey S. "Stochastic Integrals." In Stochastic World. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00071-8_5.

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Conference papers on the topic "Stochastic"

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Miękisz, Jacek. "Stochastic stability in spatial games." In Stochastic Models in Biological Sciences. Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-15.

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Mobilia, Mauro, Ivan T. Georgiev, and Uwe C. Täuber. "Spatial stochastic predator-prey models." In Stochastic Models in Biological Sciences. Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-16.

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Lipowsky, Reinhard, and Steffen Liepelt. "Molecular motors and stochastic networks." In Stochastic Models in Biological Sciences. Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-9.

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Bahuleyan, Hareesh, Lili Mou, Hao Zhou, and Olga Vechtomova. "Stochastic." In Proceedings of the 2019 Conference of the North. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-1411.

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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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Carlon, Enrico. "Thermodynamics of DNA microarrays." In Stochastic Models in Biological Sciences. Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-13.

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Claussen, Jens Christian. "Discrete stochastic processes, replicator and Fokker-Planck equations of coevolutionary dynamics in finite and infinite populations." In Stochastic Models in Biological Sciences. Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-1.

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Szabó, György. "Spreading mechanisms of cooperation for the evolutionary Prisoner's Dilemma games." In Stochastic Models in Biological Sciences. Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-10.

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Argasiński, Krzysztof. "Problems with classical models of sex-ratio evolution." In Stochastic Models in Biological Sciences. Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-11.

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Bońkowska, Katarzyna, Przemysław Biecek, Agnieszka Łaszkiewicz, and Stanisław Cebrat. "Relationship between the selection pressure and the rate of mutation accumulation." In Stochastic Models in Biological Sciences. Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-12.

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Reports on the topic "Stochastic"

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Boehm, Albert R. Stochastic Indexing. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada283143.

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Boehm, Albert R. Stochastic Indexing. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada283272.

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Platzer, Andre. Stochastic Differential Dynamic Logic for Stochastic Hybrid Programs. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada543485.

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Cormican, Kelly J., David P. Morton, and R. K. Wood. Stochastic Network Interdiction. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada491085.

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Beran, Philip S., Ned J. Lindsley, Jose Camberos, and Mohammad Kurdi. Stochastic Nonlinear Aeroelasticity. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada494780.

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Turalska, M., and B. J. West. Fractional Stochastic Individuals. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ad1003075.

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Perez-Abreu, Victor. Product Stochastic Measures. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada162833.

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X. Frank Xu. Numerical Stochastic Homogenization Method and Multiscale Stochastic Finite Element Method - A Paradigm for Multiscale Computation of Stochastic PDEs. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/1036255.

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Evans, George, Seppo Honkapohja, and Noah Williams. Generalized Stochastic Gradient Learning. National Bureau of Economic Research, 2005. http://dx.doi.org/10.3386/t0317.

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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), 1999. http://dx.doi.org/10.2172/785030.

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