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Relevant bibliographies by topics / Approximate model

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Academic literature on the topic 'Approximate model'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Approximate model"

1

Chen, Juan. "Model Approximate Method of Complex System." International Journal of Modeling and Optimization 5, no. 2 (2015): 128–34. http://dx.doi.org/10.7763/ijmo.2015.v5.448.

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Smucker, Byran J., and Nathan M. Drew. "Approximate Model Spaces for Model-Robust Experiment Design." Technometrics 57, no. 1 (2015): 54–63. http://dx.doi.org/10.1080/00401706.2014.897262.

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Lange, Kenneth. "An Approximate Model of Polygenic Inheritance." Genetics 147, no. 3 (1997): 1423–30. http://dx.doi.org/10.1093/genetics/147.3.1423.

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The finite polygenic model approximates polygenic inheritance by postulating that a quantitative trait is determined by n independent, additive loci. The 3n possible genotypes for each person in this model limit its applicability. Cannings, Thompson, and Skolnick suggested a simplified, nongenetic version of the model involving only 2n + 1 genotypes per person. This article shows that this hypergeometric polygenic model also approximates polygenic inheritance well. In particular, for noninbred pedigrees, trait means, variances, covariances, and marginal distributions match those of the ordinar

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Golubkovich, A. V., and A. N. Dadyko. "Approximate model of wet particle drying." Agricultural machinery and technologies, no. 4 (August 20, 2017): 3–8. http://dx.doi.org/10.22314/2073-7599-2017-4-3-8.

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YOSHIOKA, Muneyuki. "Approximate Model of Pressure-measuring Diaphragm." Transactions of the Society of Instrument and Control Engineers 30, no. 12 (1994): 1547–49. http://dx.doi.org/10.9746/sicetr1965.30.1547.

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Wu, Cheng-Chieh, Sven Weisbrich, and Frank Neitzel. "Approximate model for geometrical complex structures." Materials Today: Proceedings 4, no. 5 (2017): 5995–6000. http://dx.doi.org/10.1016/j.matpr.2017.06.084.

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SHI, CHANG-GUANG, and MINORU HIRAYAMA. "APPROXIMATE VORTEX SOLUTION OF FADDEEV MODEL." International Journal of Modern Physics A 23, no. 09 (2008): 1361–69. http://dx.doi.org/10.1142/s0217751x08039517.

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Through an ansatz specifying the azimuthal-angle dependence of the solution, the static field equation for vortex of the Faddeev model is converted to an algebraic ordinary differential equation. An approximate analytic expression of the vortex solution is explored so that the energy per unit vortex length becomes as small as possible. It is observed that the minimum energy of vortex is approximately proportional to the integer which specifies the solution.

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Wang, Jinyan, Minghao Yin, and Jingli Wu. "Two approximate algorithms for model counting." Theoretical Computer Science 657 (January 2017): 28–37. http://dx.doi.org/10.1016/j.tcs.2016.04.047.

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Fetecau, Razan, and Doron Levy. "Approximate Model Equations for Water Waves." Communications in Mathematical Sciences 3, no. 2 (2005): 159–70. http://dx.doi.org/10.4310/cms.2005.v3.n2.a4.

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Dunca, Argus A., and Roger Lewandowski. "Modeling error in approximate deconvolution model." Communications in Mathematical Sciences 12, no. 4 (2014): 757–78. http://dx.doi.org/10.4310/cms.2014.v12.n4.a8.

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Dissertations / Theses on the topic "Approximate model"

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Ratmann, Oliver Rene. "Approximate Bayesian Computation under model uncertainty." Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520934.

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Biris, Elias. "Approximate model composition for explanation generation." Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/23206.

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This thesis presents an automation framework for the formulation of knowledge models to support the generation of explanations for engineering systems that are represented by the resulting models. Such models are assembled from instantiated generic component descriptions, known as model fragments. The model fragments are of sufficient details that generally satisfy the requirements of information content as identified by the user asking for explanations. Using a combination of Bayesian Networks and Approximate Reasoning techniques, in order to cope with different types of uncertainty arising f

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Topan, Engin. "An Approximate Model For Kanban Controlled Assembly Systems." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606438/index.pdf.

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In this thesis, an approximation is proposed to evaluate the steady-state performance of kanban controlled assembly systems. The approximation is developed for the systems with two components making up an assembly. Then, it is extended to systems with more than two components. A continuous-time Markov model is aggregated keeping the model exact, and this aggregate model is approximated replacing some state-dependent transition rates with constant rates. Decomposition of the approximate aggregate model into submodels guarantees product-form steady-state distribution for each subsystem. Finally,

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Kao, Hai Feng. "Optimal planning with approximate model-based reinforcement learning." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/39889.

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Model-based reinforcement learning methods make efficient use of samples by building a model of the environment and planning with it. Compared to model-free methods, they usually take fewer samples to converge to the optimal policy. Despite that efficiency, model-based methods may not learn the optimal policy due to structural modeling assumptions. In this thesis, we show that by combining model- based methods with hierarchically optimal recursive Q-learning (HORDQ) under a hierarchical reinforcement learning framework, the proposed approach learns the optimal policy even when the assumptions

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Fonseca, Guilherme Dias da. "Approximate range searching in the absolute error model." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7770.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.<br>Thesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Schmid, Frank. "Essays on the large dimensional approximate dynamic factor model /." Berlin : dissertation.de, 2009. http://www.ub.unibe.ch/content/bibliotheken_sammlungen/sondersammlungen/dissen_bestellformular/index_ger.html.

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Schmid, Frank. "Essays on the large dimensional approximate dynamic factor model." Berlin dissertation.de, 2008. http://d-nb.info/999361538/04.

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Neupane, Thakur. "STAMINA: Stochastic Approximate Model-Checker for Infinite-State Analysis." DigitalCommons@USU, 2019. https://digitalcommons.usu.edu/etd/7607.

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Reliable operation of every day use computing system, from simple coffee machines to complex flight controller system in an aircraft, is necessary to save time, money, and in some cases lives. System testing can check for the presence of unwanted execution but cannot guarantee the absence of such. Probabilistic model checking techniques have demonstrated significant potential in verifying performance and reliability of various systems whose execution are defined with likelihood. However, its inability to scale limits its applicability in practice. This thesis presents a new model checker, STAM

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Tsai, Pi-Wen. "Three-level designs robust to model uncertainty." Thesis, University of Reading, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389644.

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Popoff, Evan. "An approximate spatio-temporal Bayesian model for Alberta wheat yield." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/47033.

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Crop forecasting models are very valuable to a number of agricultural and government agencies. We investigated the effect of spatial and temporal environmental climate covariates on the growth of crop yield (wheat) at the regional scale across the province of Alberta. Model fitting was accomplished using data collected during the growing season from climate stations across Alberta provided by Agriculture and Agri-Food Canada (AAFC). A best fitting model was selected which takes into account simplicity (number of covariates used) and accuracy (predictive capability based on two selection criter

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Books on the topic "Approximate model"

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Pemberton, James. Accidental bequests and approximate Ricardian equivalence in the Blanchard-Yaari model. University of Reading, Dept. of Economics, 1996.

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2

Singh, Hema, R. Chandini, and Rakesh Mohan Jha. RCS Estimation of Linear and Planar Dipole Phased Arrays: Approximate Model. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-754-3.

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Kabundi, Alain N. France in the global economy: A structural approximate dynamic factor model analysis. International Monetary Fund, European Dept., 2007.

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Connor, Gregory. A test for the number of factors in an approximate factor model. LSE Financial Markets Group, 1992.

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Connor, Gregory. A test for the number of factors in an approximate factor model. London School of Economics, Financial Markets Group, 1992.

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6

Data analysis and approximate models: Model choice, location-scale, analysis of variance, nonparametic regression and image analysis. CRC Press, 2014.

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7

Davis, G. B. A model of oxidation in pyritic mine wastes: Part 1, equations and approximate solution. s.n, 1986.

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Davis, G. B. A model of oxidation in pyritic mine wastes: Part 2, comparison of numerical and approximate solutions. s.n, 1986.

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9

Mántaras, Ramon López de. Approximate reasoning models. E. Horwood, 1990.

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10

1930-, Roth Robert, ed. Methods in approximation: Techniques for mathematical modelling. D. Reidel Pub. Co., 1986.

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Book chapters on the topic "Approximate model"

1

Huber, Peter J. "Approximate Models." In Goodness-of-Fit Tests and Model Validity. Birkhäuser Boston, 2002. http://dx.doi.org/10.1007/978-1-4612-0103-8_3.

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Hérault, Thomas, Richard Lassaigne, Frédéric Magniette, and Sylvain Peyronnet. "Approximate Probabilistic Model Checking." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24622-0_8.

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Marin, Jean-Michel, Pierre Pudlo, Arnaud Estoup, and Christian Robert. "Likelihood-Free Model Choice." In Handbook of Approximate Bayesian Computation. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315117195-6.

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Heij, C., and J. C. Willems. "A Deterministic Approach to Approximate Modelling." In From Data to Model. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-75007-6_3.

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Michel, J., S. Pfeiffer, W. Schulz, M. Nießen, and V. Kostrykin. "Approximate Model for Laser Welding." In Nonlinear Dynamics of Production Systems. Wiley-VCH Verlag GmbH & Co. KGaA, 2005. http://dx.doi.org/10.1002/3527602585.ch24.

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Chakraborty, Supratik, Kuldeep S. Meel, and Moshe Y. Vardi. "A Scalable Approximate Model Counter." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40627-0_18.

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Grancharova, Alexandra, and Tor Arne Johansen. "Explicit NMPC via Approximate mp-NLP." In Explicit Nonlinear Model Predictive Control. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0_4.

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Kazar, Baris M., and Mete Celik. "Comparing Exact and Approximate SAR Model Solutions." In Spatial AutoRegression (SAR) Model. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1842-9_4.

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Kazar, Baris M., and Mete Celik. "Parallel Implementations of Approximate SAR Model Solutions." In Spatial AutoRegression (SAR) Model. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1842-9_5.

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Allgöwer, F., and F. J. Doyle. "Approximate I/O-Linearization of Nonlinear Systems." In Nonlinear Model Based Process Control. Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5094-1_9.

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Conference papers on the topic "Approximate model"

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van Bremen, Timothy, and Ondrej Kuzelka. "Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/587.

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We study the symmetric weighted first-order model counting task and present ApproxWFOMC, a novel anytime method for efficiently bounding the weighted first-order model count of a sentence given an unweighted first-order model counting oracle. The algorithm has applications to inference in a variety of first-order probabilistic representations, such as Markov logic networks and probabilistic logic programs. Crucially for many applications, no assumptions are made on the form of the input sentence. Instead, the algorithm makes use of the symmetry inherent in the problem by imposing cardinality c

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Andrews, Burton W., Pablo A. Iglesias, and Eduardo D. Sontag. "Signal Detection and Approximate Adaptation Implies an Approximate Internal Model." In Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.377227.

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Su, Bing, Jiaqi Zhou, Liu Zhou, Hao Ji, and Guohui Lin. "Approximate Algorithm for Two-Echelon Vehicle Routing." In 2019 International Conference on Economic Management and Model Engineering (ICEMME). IEEE, 2019. http://dx.doi.org/10.1109/icemme49371.2019.00092.

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Lawrynczuk, Maciej. "Approximate state-space model predictive control." In 2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR ). IEEE, 2015. http://dx.doi.org/10.1109/mmar.2015.7283973.

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Chuang, Frank, Claus Danielson, and Francesco Borrelli. "Robust approximate symmetric model predictive control." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402567.

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Martinek, Tomas, Jan Vozenilek, and Matej Lexa. "Architecture model for approximate palindrome detection." In 2009 12th IEEE International Symposium on Design and Diagnostics of Electronic Circuits & Systems. IEEE, 2009. http://dx.doi.org/10.1109/ddecs.2009.5012105.

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Zhu, Weijun, Pan Feng, and Miaolei Deng. "An Approximate CTL Model Checking Approach." In 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2019. http://dx.doi.org/10.1109/icsess47205.2019.9040772.

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Nam, Nguyen Hoai, Sorin Olaru, and Morten Hovd. "Patchy approximate explicit model predictive control." In 2010 International Conference on Control, Automation and Systems (ICCAS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccas.2010.5670223.

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Asmara, Andi, Ubald Nienhuis, and Robert Hekkenberg. "Approximate orthogonal simplification of 3D model." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586162.

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Pavlov, Dmitry, and Padhraic Smyth. "Approximate Query Answering by Model Averaging." In Proceedings of the 2003 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2003. http://dx.doi.org/10.1137/1.9781611972733.13.

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Reports on the topic "Approximate model"

1

Govindaraju, Shankar G., and David L. Dill. Approximate Symbolic Model Checking Using Overlapping Projections. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada401014.

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Dechant, Lawrence. Approximate Model for Turbulent Stagnation Point Flow. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1235211.

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Niekum, Scott, Sarah Osentoski, Christopher G. Atkeson, and Andrew G. Barto. CHAMP: Changepoint Detection Using Approximate Model Parameters. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada605983.

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Ephremides, A., and Rong-Zhu Zhu. Delay Analysis of Interacting Queues with an Approximate Model. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada452458.

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Tseng, Lewis, and Nitin Vaidya. Iterative Approximate Byzantine Consensus under a Generalized Fault Model. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada564090.

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Fudenberg, Drew, David K. Levine, and Zacharias Maniadis. An Approximate Dual-Self Model and Paradoxes of Choice under Risk. Federal Reserve Bank of St. Louis, 2012. http://dx.doi.org/10.20955/wp.2012.034.

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Bott, T. F., S. W. Eisenhawer, and S. F. Agnew. Evaluation of high-level waste pretreatment processes with an approximate reasoning model. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/334243.

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Zimmerman, T. An approximate HSPICE model for orbit low noise analog bipolar NPN transistors. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/5475545.

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Dechant, Lawrence J. Estimation of Several Turbulent Fluctuation Quantities Using an Approximate Pulsatile Flow Model. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1331517.

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Kim, Kang Seog, Kevin T. Clarno, Yuxuan Liu, Xinyan Wang, William R. Martin, and Benjamin S. Collins. Neutron Capture Energies for Flux Normalization and Approximate Model for Gamma-Smeared Power. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1437916.

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