Academic literature on the topic 'Stochastic parameters modelling'
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Journal articles on the topic "Stochastic parameters modelling"
Beutler, G., A. Jäggi, U. Hugentobler, and L. Mervart. "Efficient satellite orbit modelling using pseudo-stochastic parameters." Journal of Geodesy 80, no. 7 (August 24, 2006): 353–72. http://dx.doi.org/10.1007/s00190-006-0072-6.
Full textBorzunov, S. V., M. E. Semenov, N. I. Sel’vesyuk, and P. A. Meleshenko. "Hysteretic Converters with Stochastic Parameters." Mathematical Models and Computer Simulations 12, no. 2 (March 2020): 164–75. http://dx.doi.org/10.1134/s2070048220020040.
Full textEllam, L., M. Girolami, G. A. Pavliotis, and A. Wilson. "Stochastic modelling of urban structure." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, no. 2213 (May 2018): 20170700. http://dx.doi.org/10.1098/rspa.2017.0700.
Full textMarano, Giuseppe Carlo, Mariantonietta Morga, and Sara Sgobba. "Parameters Identification of Stochastic Nonstationary Process Used in Earthquake Modelling." International Journal of Geosciences 04, no. 02 (2013): 290–301. http://dx.doi.org/10.4236/ijg.2013.42027.
Full textIntsiful, Joseph, and Harald Kunstmann. "Upscaling of Land-Surface Parameters Through Inverse Stochastic SVAT-Modelling." Boundary-Layer Meteorology 129, no. 1 (September 13, 2008): 137–58. http://dx.doi.org/10.1007/s10546-008-9303-0.
Full textDørum, C., O. S. Hopperstad, T. Berstad, and D. Dispinar. "Numerical modelling of magnesium die-castings using stochastic fracture parameters." Engineering Fracture Mechanics 76, no. 14 (September 2009): 2232–48. http://dx.doi.org/10.1016/j.engfracmech.2009.07.001.
Full textFrederiksen, Jorgen S., Terence J. O'Kane, and Meelis J. Zidikheri. "Subgrid modelling for geophysical flows." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1982 (January 13, 2013): 20120166. http://dx.doi.org/10.1098/rsta.2012.0166.
Full textRomansky, Radi. "Mathematical Modelling and Study of Stochastic Parameters of Computer Data Processing." Mathematics 9, no. 18 (September 12, 2021): 2240. http://dx.doi.org/10.3390/math9182240.
Full textGassmann, H. I., and G. Infanger. "Modelling history-dependent parameters in the SMPS format for stochastic programming." IMA Journal of Management Mathematics 19, no. 1 (February 2, 2007): 87–97. http://dx.doi.org/10.1093/imaman/dpm006.
Full textARTEMEV, S. S., and M. A. YAKUNIN. "Estimation of the parameters in stochastic differential equations." Russian Journal of Numerical Analysis and Mathematical Modelling 12, no. 1 (1997): 1–12. http://dx.doi.org/10.1515/rnam.1997.12.1.1.
Full textDissertations / Theses on the topic "Stochastic parameters modelling"
Kucharska, Magdalena, and Jolanta Pielaszkiewicz. "NIG distribution in modelling stock returns with assumption about stochastic volatility : Estimation of parameters and application to VaR and ETL." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-2874.
Full textWe model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatility. We consider different methods of parametrization of returns and following the paper of Lindberg, [21] we
assume that the volatility is a linear function of the number of trades. In addition to the Lindberg’s paper, we suggest daily stock volumes and amounts as alternative measures of the volatility.
As an application of the models, we perform Value-at-Risk and Expected Tail Loss predictions by the Lindberg’s volatility model and by our own suggested model. These applications are new and not described in the
literature. For better understanding of our caluclations, programmes and simulations, basic informations and properties about the Normal Inverse Gaussian and Inverse Gaussian distributions are provided. Practical applications of the models are implemented on the Nasdaq-OMX, where we have calculated Value-at-Risk and Expected Tail Loss
for the Ericsson B stock data during the period 1999 to 2004.
Kucharska, Magdalena, and Jolanta Maria Pielaszkiewicz. "NIG distribution in modelling stock returns with assumption about stochastic volatility : Estimation of parameters and application to VaR and ETL." Thesis, Halmstad University, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-58180.
Full textMesbahi, Abdessamad. "Deterministic and Stochastic Economic Modeling of Hybrid Power Supply System with Photovoltaic Generators." Master's thesis, КПІ ім. Ігоря Сікорського, 2021. https://ela.kpi.ua/handle/123456789/42555.
Full textChen, Liang. "Small population bias and sampling effects in stochastic mortality modelling." Thesis, Heriot-Watt University, 2017. http://hdl.handle.net/10399/3372.
Full textLück, Alexander Tobias [Verfasser]. "Stochastic spatial modelling of DNA methylation patterns and moment-based parameter estimation / Alexander Tobias Lück." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1219068659/34.
Full textTran, The Truyen. "On conditional random fields: applications, feature selection, parameter estimation and hierarchical modelling." Curtin University of Technology, Dept. of Computing, 2008. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=18614.
Full textOn the theory side, the thesis addresses three important theoretical issues of CRFs: feature selection, parameter estimation and modelling recursive sequential data. These issues are all addressed under a general setting of partial supervision in that training labels are not fully available. For feature selection, we introduce a novel learning algorithm called AdaBoost.CRF that incrementally selects features out of a large feature pool as learning proceeds. AdaBoost.CRF is an extension of the standard boosting methodology to structured and partially observed data. We demonstrate that the AdaBoost.CRF is able to eliminate irrelevant features and as a result, returns a very compact feature set without significant loss of accuracy. Parameter estimation of CRFs is generally intractable in arbitrary network structures. This thesis contributes to this area by proposing a learning method called AdaBoost.MRF (which stands for AdaBoosted Markov Random Forests). As learning proceeds AdaBoost.MRF incrementally builds a tree ensemble (a forest) that cover the original network by selecting the best spanning tree at a time. As a result, we can approximately learn many rich classes of CRFs in linear time. The third theoretical work is on modelling recursive, sequential data in that each level of resolution is a Markov sequence, where each state in the sequence is also a Markov sequence at the finer grain. One of the key contributions of this thesis is Hierarchical Conditional Random Fields (HCRF), which is an extension to the currently popular sequential CRF and the recent semi-Markov CRF (Sarawagi and Cohen, 2004). Unlike previous CRF work, the HCRF does not assume any fixed graphical structures.
Rather, it treats structure as an uncertain aspect and it can estimate the structure automatically from the data. The HCRF is motivated by Hierarchical Hidden Markov Model (HHMM) (Fine et al., 1998). Importantly, the thesis shows that the HHMM is a special case of HCRF with slight modification, and the semi-Markov CRF is essentially a flat version of the HCRF. Central to our contribution in HCRF is a polynomial-time algorithm based on the Asymmetric Inside Outside (AIO) family developed in (Bui et al., 2004) for learning and inference. Another important contribution is to extend the AIO family to address learning with missing data and inference under partially observed labels. We also derive methods to deal with practical concerns associated with the AIO family, including numerical overflow and cubic-time complexity. Finally, we demonstrate good performance of HCRF against rivals on two applications: indoor video surveillance and noun-phrase chunking.
Gallet, Emmanuelle. "Techniques de model-checking pour l’inférence de paramètres et l’analyse de réseaux biologiques." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC035/document.
Full textIn this thesis, we present the use of model checking techniques for inference of parameters of Gene Regulatory Networks (GRNs) and formal analysis of a signalling pathway. In the first and main part, we provide an approach to infer biological parameters governing the dynamics of discrete models of GRNs. GRNs are encoded in the form of a meta-model, called Parametric GRN, such that a parameter instance defines a discrete model of the original GRN. Provided that targeted biological properties are expressed in the form of LTL formulas, LTL model-checking techniques are combined with symbolic execution and constraint solving techniques to select discrete models satisfying these properties. The challenge is to prevent combinatorial explosion in terms of size and number of discrete models. Our method is implemented in Java, in a tool called SPuTNIk. The second part describes a work performed in collaboration with child neurologists, who aim to understand the occurrence of toxic or protective phenotype of microglia (a type of macrophage in the brain) in the case of preemies. We use an other type of model-checking, the statistical model-checking, to study a particular type of biological network: the Wnt/β- catenin pathway that transmits an external signal into the cells via a cascade of biochemical reactions. Here we present the benefit of the stochastic model checker COSMOS, using the Hybrid Automata Stochastic Logic (HASL), that is an very expressive formalism allowing a sophisticated formal analysis of the dynamics of the Wnt/β-catenin pathway, modelled as a discrete event stochastic process
Abhinav, S. "Stochastic Modelling of Vehicle-Structure Interactions : Dynamic State And Parameter Estimation, And Global Response Sensitivity Analysis." Thesis, 2016. http://etd.iisc.ernet.in/handle/2005/2736.
Full textVarziri, M. Saeed. "Parameter estimation in nonlinear continuous-time dynamic models with modelling errors and process disturbances." Thesis, 2008. http://hdl.handle.net/1974/1248.
Full textThesis (Ph.D, Chemical Engineering) -- Queen's University, 2008-06-20 16:34:44.586
Books on the topic "Stochastic parameters modelling"
Quintana, José Mario, Carlos Carvalho, James Scott, and Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.
Full textBook chapters on the topic "Stochastic parameters modelling"
Meshram, Deodas T., V. T. Jadhav, S. D. Gorantiwar, and Ram Chandra. "Modeling of Weather Parameters Using Stochastic Methods." In Climate Change Modelling, Planning and Policy for Agriculture, 67–77. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2157-9_8.
Full textAl-Ani, Tarik, and Yskander Hamam. "Parameters identification of a time-varying stochastic dynamic systems using Viterbi algorithm." In System Modelling and Optimization, 567–73. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-0-387-34897-1_69.
Full textPurutçuoğlu, Vilda. "Stochastic Modelling of Biochemical Networks and Inference of Model Parameters." In Springer Proceedings in Mathematics & Statistics, 369–85. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74086-7_18.
Full textMa, Yue, Jiaxin Liu, and Xuzhao Hou. "Stochastic Dynamics Modelling of Hybrid Electrical Vehicle and Parameters Estimation." In Lecture Notes in Electrical Engineering, 349–61. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6320-8_37.
Full textVidhya, C., and P. Balasubramaniam. "Stability of Uncertain Reaction-Diffusion Stochastic BAM Neural Networks with Mixed Delays and Markovian Jumping Parameters." In Mathematical Modelling and Scientific Computation, 283–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28926-2_30.
Full textHe, Zhongjie, Hua Li, and Karl Erik Birgersson. "Integrated Stochastic and Deterministic Sensitivity Analysis: Correlating Variability of Design Parameters with Cell and Stack Performance." In Reduced Modelling of Planar Fuel Cells, 227–69. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42646-4_6.
Full textWanduku, Divine, C. Newman, O. Jegede, and B. Oluyede. "Modeling the Stochastic Dynamics of Influenza Epidemics with Vaccination Control, and the Maximum Likelihood Estimation of Model Parameters." In Mathematical Modelling in Health, Social and Applied Sciences, 23–72. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2286-4_2.
Full textMohanty, Ashrumochan, Satish Chandra Bhuyan, and Sangeeta Kumari. "Impact of Different Parameters in the Development of Operating Policies of a Reservoir Using Stochastic Dynamic Modelling Technique." In Lecture Notes in Civil Engineering, 353–69. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4629-4_25.
Full textLiptser, Robert S., and Albert N. Shiryaev. "Parameter Estimation and Testing of Statistical Hypotheses for Diffusion-Type Processes." In Stochastic Modelling and Applied Probability, 219–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-10028-8_7.
Full textStettner, L. "Adaptive control of semilinear stochastic evolution equations." In Modelling and Optimization of Distributed Parameter Systems Applications to engineering, 278–86. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-0-387-34922-0_29.
Full textConference papers on the topic "Stochastic parameters modelling"
Kumar, Rahul, Sayan Gupta, and Shaikh Faruque Ali. "Stochastic Modelling and Analysis of Rotating Bladed Discs." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-16212.
Full textGorobetz, M., and A. Levchenkov. "Modelling Of Stochastic Parameters For Control Of City Electric Transport Systems Using Evolutionary Algorithm." In 22nd Conference on Modelling and Simulation. ECMS, 2008. http://dx.doi.org/10.7148/2008-0207.
Full textPITZ, EMIL J., SEAN ROONEY, and KISHORE V. POCHIRAJU. "Stochastic Modelling of Additively Manufactured Structures Using a Neural Network for Identification of Random Field Parameters." In American Society for Composites 2020. Lancaster, PA: DEStech Publications, Inc., 2020. http://dx.doi.org/10.12783/asc35/34974.
Full textVanem, Erik. "Stochastic Models for Long-Term Prediction of Extreme Waves: A Literature Survey." In ASME 2010 29th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/omae2010-20076.
Full textWegener, Konrad, Guilherme E. Vargas, Friedrich Kuster, Fa´bio W. Pinto, and Thomas Schnider. "Modelling of Hard Broaching." In ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59454.
Full textPukl, Radomír, David Lehký, and Drahomír Novák. "Towards nonlinear reliability assessment of concrete transport structures." In IABSE Conference, Kuala Lumpur 2018: Engineering the Developing World. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2018. http://dx.doi.org/10.2749/kualalumpur.2018.0330.
Full textPozo Dominguez, Alejandro, Nicholas Hills, and Simao Marques. "Sensitivity Analysis and Uncertainty Quantification for Rim Seal Ingestion With 1-D Network Models." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15218.
Full textMercatali, Luigi, Yousef Alzaben, and Victor Hugo Sanchez Espinoza. "Propagation of Nuclear Data Uncertainties in PWR Pin-Cell Burnup Calculations via Stochastic Sampling." In 2018 26th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/icone26-81711.
Full textNaseh Moghanlou, Lida, and Mohammad Pourgol-Mohammad. "Assessment of the Pitting Corrosion Degradation Lifetime: Case Study of Boiler Tubes." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51854.
Full textVasilj, Andrej, Sebastian Schuster, and Alexander White. "The Influence of Wake Chopping on Wet-Steam Turbine Modelling." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15766.
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