Relevant bibliographies by topics / STOCHASTIC SENSITIVITY

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

Academic literature on the topic 'STOCHASTIC SENSITIVITY'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "STOCHASTIC SENSITIVITY"

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Eschenbach, Ted G., and Robert J. Gimpel. "Stochastic Sensitivity Analysis." Engineering Economist 35, no. 4 (January 1990): 305–21. http://dx.doi.org/10.1080/00137919008903024.

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Irving, A. D. "Stochastic sensitivity analysis." Applied Mathematical Modelling 16, no. 1 (January 1992): 3–15. http://dx.doi.org/10.1016/0307-904x(92)90110-o.

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Zhou, Peiyuan, and Jinling Wang. "Stochastic Ionosphere Models for Precise GNSS Positioning: Sensitivity Analysis." Journal of Global Positioning Systems 12, no. 1 (June 30, 2013): 53–60. http://dx.doi.org/10.5081/jgps.12.1.53.

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Bashkirtseva, I. A., and L. B. Ryashko. "Stochastic sensitivity of 3D-cycles." Mathematics and Computers in Simulation 66, no. 1 (June 2004): 55–67. http://dx.doi.org/10.1016/j.matcom.2004.02.021.

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Ryashko, L. B., and I. A. Bashkirtseva. "On control of stochastic sensitivity." Automation and Remote Control 69, no. 7 (July 2008): 1171–80. http://dx.doi.org/10.1134/s0005117908070084.

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McClendon, Marvin, and Herschel Rabitz. "Sensitivity analysis in stochastic mechanics." Physical Review A 37, no. 9 (May 1, 1988): 3493–98. http://dx.doi.org/10.1103/physreva.37.3493.

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Kundu, Ajanta, and Sandip Sarkar. "Stochastic resonance in visual sensitivity." Biological Cybernetics 109, no. 2 (November 15, 2014): 241–54. http://dx.doi.org/10.1007/s00422-014-0638-y.

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Römisch, Werner, and Rüdiger Schultz. "Distribution sensitivity in stochastic programming." Mathematical Programming 50, no. 1-3 (March 1991): 197–226. http://dx.doi.org/10.1007/bf01594935.

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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. The conclusion indicates that solutions of the ERM model are robust in the sense that they may have a minimum sensitivity with respect to random parameter variations in stochastic complementarity problems. On the other hand, we employ quasi-Monte Carlo methods and derivative-free methods to solve ERM model.
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Grzywiński, Maksym. "Stochastic Sensitivity Analysis of Cylindrical Shell." Transactions of the VŠB – Technical University of Ostrava, Civil Engineering Series. 16, no. 2 (December 1, 2016): 35–42. http://dx.doi.org/10.1515/tvsb-2016-0012.

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Abstract The paper deals with some chosen aspects of stochastic sensitivity structural analysis and its application in the engineering practice. The main aim of the study is to provide the generalized stochastic perturbation technique based on classical Taylor expansion with a single random variable. The study is illustrated by numerical results concerning an industrial thin shell structure modeled as a 3-D structure.
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Dissertations / Theses on the topic "STOCHASTIC SENSITIVITY"

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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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Bouakiz, Mokrane. "Risk-sensitivity in stochastic optimization with applications." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/25457.

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Ghalebsaz-Jeddi, Babak. "Analysis and sensitivity of stochastic capacitatied multi-commodity flows." Cincinnati, Ohio : University of Cincinnati, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=ucin1078512441.

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Long, Sally. "Evaluating farm management strategy using sensitivity and stochastic analysis." Thesis, Kansas State University, 2013. http://hdl.handle.net/2097/19756.

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Master of Agribusiness
Department of Agricultural Economics
Jason Bergtold
The dramatic changes that have taken place in the production agriculture industry in the last decade have the Long Family Partnership wanting to reassess their farm land management strategy. As land owners, they feel as though they might be missing out on profit opportunity by continuing their current lease agreements as status quo. The objective of this research is to determine the optimal land management strategy for the Partnership farm that maximizes net returns for crop production, but also taking into account input costs and risk. Three scenarios were built: (1) a Base Case of the current share-crop and cash lease Agreements; (2) the possibility of farming their own irrigated farm land and continuing to cash lease land used to produce dryland wheat; and (3) deciding to farm all the irrigated and dry land farm acreage themselves. In order to do this, a whole-farm budget spreadsheet model was generated to assess alternative land management scenarios. The difference in net returns between alternative land rental scenarios were then compared and followed by a sensitivity analysis and stochastic analysis using @RISK software. The findings concluded that there was greater potential to increase net farm income while still conservatively managing risk by investing into their own farm land, as not only owners but also as operators. The stochastic and sensitivity analysis confirmed that farming their own land was more sensitive to changes in yields, prices and input expenses. However, even in consideration of the additional risk, the probability of increasing net farm income was greater for the scenarios in which they farmed their own land.
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GHALEBSAZ-JEDDI, BABAK. "ANALYSIS AND SENSITIVITY OF STOCHASTIC CAPACITATED MULTI-COMMODITY FLOWS." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1078512441.

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Koch, Stefan [Verfasser], and Andreas [Akademischer Betreuer] Neuenkirch. "Sensitivity results in stochastic analysis / Stefan Koch ; Betreuer: Andreas Neuenkirch." Mannheim : Universitätsbibliothek Mannheim, 2019. http://d-nb.info/1195441541/34.

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Koch, Stefan Verfasser], and Andreas [Akademischer Betreuer] [Neuenkirch. "Sensitivity results in stochastic analysis / Stefan Koch ; Betreuer: Andreas Neuenkirch." Mannheim : Universitätsbibliothek Mannheim, 2019. http://nbn-resolving.de/urn:nbn:de:bsz:180-madoc-520185.

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Fadikar, Arindam. "Stochastic Computer Model Calibration and Uncertainty Quantification." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/91985.

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This dissertation presents novel methodologies in the field of stochastic computer model calibration and uncertainty quantification. Simulation models are widely used in studying physical systems, which are often represented by a set of mathematical equations. Inference on true physical system (unobserved or partially observed) is drawn based on the observations from corresponding computer simulation model. These computer models are calibrated based on limited ground truth observations in order produce realistic predictions and associated uncertainties. Stochastic computer model differs from traditional computer model in the sense that repeated execution results in different outcomes from a stochastic simulation. This additional uncertainty in the simulation model requires to be handled accordingly in any calibration set up. Gaussian process (GP) emulator replaces the actual computer simulation when it is expensive to run and the budget is limited. However, traditional GP interpolator models the mean and/or variance of the simulation output as function of input. For a simulation where marginal gaussianity assumption is not appropriate, it does not suffice to emulate only the mean and/or variance. We present two different approaches addressing the non-gaussianity behavior of an emulator, by (1) incorporating quantile regression in GP for multivariate output, (2) approximating using finite mixture of gaussians. These emulators are also used to calibrate and make forward predictions in the context of an Agent Based disease model which models the Ebola epidemic outbreak in 2014 in West Africa. The third approach employs a sequential scheme which periodically updates the uncertainty inn the computer model input as data becomes available in an online fashion. Unlike other two methods which use an emulator in place of the actual simulation, the sequential approach relies on repeated run of the actual, potentially expensive simulation.
Doctor of Philosophy
Mathematical models are versatile and often provide accurate description of physical events. Scientific models are used to study such events in order to gain understanding of the true underlying system. These models are often complex in nature and requires advance algorithms to solve their governing equations. Outputs from these models depend on external information (also called model input) supplied by the user. Model inputs may or may not have a physical meaning, and can sometimes be only specific to the scientific model. More often than not, optimal values of these inputs are unknown and need to be estimated from few actual observations. This process is known as inverse problem, i.e. inferring the input from the output. The inverse problem becomes challenging when the mathematical model is stochastic in nature, i.e., multiple execution of the model result in different outcome. In this dissertation, three methodologies are proposed that talk about the calibration and prediction of a stochastic disease simulation model which simulates contagion of an infectious disease through human-human contact. The motivating examples are taken from the Ebola epidemic in West Africa in 2014 and seasonal flu in New York City in USA.
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Azim, Qurat-Ul-Ain. "Information theoretic framework for stochastic sensitivity and specificity analysis in biochemical networks." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/52712.

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Biochemical reaction networks involve many chemical species and are inherently stochastic and complex in nature. Reliable and organised functioning of such systems in varied environments requires that their behaviour is robust with respect to certain parameters while sensitive to other variations, and that they exhibit specific responses to various stimuli. There is a continuous need for improved models and methodologies to unravel the complex behaviour of the dynamics of such systems. In this thesis, we apply ideas from information theory to develop novel methods to study properties of biochemical networks. In the first part of the thesis, a framework for the study of parametric sensitivity in stochastic models of biochemical networks using entropies and mutual information is developed. The concept of noise entropy is introduced and its interplay with parametric sensitivity is studied as the system becomes more stochastic. Using the methodology for gene expression models, it is shown that noise can change the sensitivities of the system at var- ious orders of parameter interaction. An approximate and computationally more efficient way of calculating the sensitivities is also developed using unscented transform. Finally, the methodology is applied to a circadian clock model, illustrating the applicability of the approach to more complex systems. In the second part of the thesis, a novel method for specificity quantification in a receptor-ligand binding system is proposed in terms of mutual information estimates be- tween appropriate stimulus and system response. The maximum specificity of 2 × 2 affinity matrices in a parametric setup is theoretically studied. Parameter optimisation methodology and specificity upper bounds are presented for maximum specificity estimates of a given affinity matrix. The quantification framework is then applied to experimental data from T-Cell signalling. Finally, generalisation of the scheme for stochastic systems is discussed.
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Wei, Xiaofan. "Stochastic Analysis and Optimization of Structures." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1163789451.

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Books on the topic "STOCHASTIC SENSITIVITY"

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Zheng, Yu-Sheng. A sensitivity analysis of stochastic inventory systems. Fontainebleau, France: INSEAD, 1992.

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Böttcher, K. J. Efficiency comparison and parameter sensitivity of deterministic and stochastic search methods. Neubiberg: University of the Federal Armed Forces Munich, Faculty of Aero-Space Engineering, Institute of Mathematics and Computer Sciences, 1996.

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Rubinstein, Reuven Y. Discrete event systems: Sensitivity analysisand stochastic optimization by the score function method. Chichester: Wiley, 1993.

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Rubinstein, Reuven Y. Discrete event systems: Sensitivity analysis and stochastic optimization by the score function method. Chichester [England]: Wiley, 1993.

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1959-, Pierre Christophe, and United States. National Aeronautics and Space Administration., eds. Stochastic sensitivity measure for mistuned high-performance turbines. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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Cao, Xi-Ren. Stochastic Learning and Optimization: A Sensitivity-Based Approach. Springer, 2010.

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Cao, Xi-Ren. Stochastic Learning and Optimization: A Sensitivity-Based Approach (International Series on Discrete Event Dynamic Systems). Springer, 2007.

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Book chapters on the topic "STOCHASTIC SENSITIVITY"

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Cao, Xi-Ren. "Constructing Sensitivity Formulas." In Stochastic Learning and Optimization, 455–86. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-69082-7_9.

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Takeuchi, Atsushi. "Sensitivity Analysis for Jump Processes." In Stochastic Analysis with Financial Applications, 207–19. Basel: Springer Basel, 2011. http://dx.doi.org/10.1007/978-3-0348-0097-6_14.

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Socha, L., and G. Zasucha. "The Sensitivity Analysis of Stochastic Hysteretic Dynamic Systems." In Computational Stochastic Mechanics, 71–79. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3692-1_7.

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Vladimirou, Hercules, and Stavros A. Zenios. "Stochastic Programming and Robust Optimization." In Advances in Sensitivity Analysis and Parametric Programming, 411–63. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6103-3_12.

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Li, Quan-Lin. "Sensitivity Analysis and Evolutionary Games." In Constructive Computation in Stochastic Models with Applications, 574–651. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11492-2_11.

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Ren, Xuchun, and Xiaodong Zhang. "Stochastic Sensitivity Analysis for Robust Topology Optimization." In Advances in Structural and Multidisciplinary Optimization, 334–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67988-4_26.

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Rong, Tongwen, Huachang Gong, and Wing W. Y. Ng. "Stochastic Sensitivity Oversampling Technique for Imbalanced Data." In Communications in Computer and Information Science, 161–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45652-1_18.

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Mangoubi, Rami S. "Stochastic Interpretation of Robust Estimation: Risk Sensitivity." In Robust Estimation and Failure Detection, 85–97. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1586-1_4.

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Pedersen, Lars, and Christian Frier. "Sensitivity Study of Stochastic Walking Load Models." In Dynamics of Bridges, Volume 5, 163–70. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9825-5_17.

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Mottershead, John E., Michael Link, Tiago A. N. Silva, Yves Govers, and Hamed Haddad Khodaparast. "The Sensitivity Method in Stochastic Model Updating." In Mechanisms and Machine Science, 65–77. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09918-7_5.

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Conference papers on the topic "STOCHASTIC SENSITIVITY"

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Sarkar, Abhijit, and Roger Ghanem. "Sensitivity Analysis of Stochastic Systems." In 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
14th AIAA/ASME/AHS Adaptive Structures Conference
7th. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-1988.

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Zarkesh-Ha, Payman, and Ken Doniger. "Stochastic interconnect layout sensitivity model." In the 2007 international workshop. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1231956.1231959.

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Ahlers, Volker. "Statistical theory for the coupling sensitivity of chaos." In Stochastic and chaotic dynamics in the lakes. AIP, 2000. http://dx.doi.org/10.1063/1.1302420.

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Tang, Gary, Gianluca Iaccarino, and Michael Eldred. "Global Sensitivity Analysis for Stochastic Collocation." In 51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
18th AIAA/ASME/AHS Adaptive Structures Conference
12th. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-2922.

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Daniel, Gregory Bregion, Leonardo Carpinetti Vieira, and Katia Lucchesi Cavalca. "Sensitivity analysis of the dynamic characteristics of thrust bearings." In 3rd International Symposium on Uncertainty Quantification and Stochastic Modeling. Rio de Janeiro, Brazil: ABCM Brazilian Society of Mechanical Sciences and Engineering, 2015. http://dx.doi.org/10.20906/cps/usm-2016-0042.

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"Stochastic sensitivity analysis of glyphosate biochemical degradation." In 22nd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2017. http://dx.doi.org/10.36334/modsim.2017.b3.lacecelia.

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Bashkirtseva, I. "Controlling stochastic sensitivity by the dynamic regulators." In APPLICATION OF MATHEMATICS IN TECHNICAL AND NATURAL SCIENCES: 9th International Conference for Promoting the Application of Mathematics in Technical and Natural Sciences - AMiTaNS’17. Author(s), 2017. http://dx.doi.org/10.1063/1.5007374.

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Jinasena, K. D. S., and D. U. J. Sonnadara. "Stochastic simulation of trees with environmental sensitivity." In SIGGRAPH Asia 2012 Posters. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2407156.2407196.

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Susnjara, Anna, Dragan Poljak, Frano Rezo, and Josip Matkovic. "Stochastic Sensitivity Analysis of Bioheat Transfer Equation." In 2019 URSI International Symposium on Electromagnetic Theory (EMTS). IEEE, 2019. http://dx.doi.org/10.23919/ursi-emts.2019.8931464.

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K. G., Papakonstantinou, and Shinozuka M. "Spatial Stochastic and Sensitivity Analysis of Steel Corrosion in a RC Port Structure." In 6th International Conference on Computational Stochastic Mechanics. Singapore: Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p050.

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Reports on the topic "STOCHASTIC SENSITIVITY"

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Baldivieso, Sebastian. Sensitivity Diagnostics and Adaptive Tuning of the Multivariate Stochastic Volatility Model. Portland State University Library, February 2020. http://dx.doi.org/10.15760/etd.7296.

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Rojas-Bernal, Alejandro, and Mauricio Villamizar-Villegas. Pricing the exotic: Path-dependent American options with stochastic barriers. Banco de la República de Colombia, March 2021. http://dx.doi.org/10.32468/be.1156.

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We develop a novel pricing strategy that approximates the value of an American option with exotic features through a portfolio of European options with different maturities. Among our findings, we show that: (i) our model is numerically robust in pricing plain vanilla American options; (ii) the model matches observed bids and premiums of multidimensional options that integrate Ratchet, Asian, and Barrier characteristics; and (iii) our closed-form approximation allows for an analytical solution of the option’s greeks, which characterize the sensitivity to various risk factors. Finally, we highlight that our estimation requires less than 1% of the computational time compared to other standard methods, such as Monte Carlo simulations.
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