Littérature scientifique sur le sujet « Simulation-Based Inference »
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Articles de revues sur le sujet "Simulation-Based Inference"
Gourieroux, Christian, et Alain Monfort. « Simulation-based inference ». Journal of Econometrics 59, no 1-2 (septembre 1993) : 5–33. http://dx.doi.org/10.1016/0304-4076(93)90037-6.
Texte intégralVogelsang, T. J. « Simulation-Based Inference in Econometrics ». Journal of the American Statistical Association 97, no 458 (juin 2002) : 657. http://dx.doi.org/10.1198/jasa.2002.s478.
Texte intégralBrehmer, Johann. « Simulation-based inference in particle physics ». Nature Reviews Physics 3, no 5 (22 mars 2021) : 305. http://dx.doi.org/10.1038/s42254-021-00305-6.
Texte intégralSheu, Ching-fan, et Suzanne L. O’Curry. « Simulation-based bayesian inference using BUGS ». Behavior Research Methods, Instruments, & ; Computers 30, no 2 (juin 1998) : 232–37. http://dx.doi.org/10.3758/bf03200649.
Texte intégralCranmer, Kyle, Johann Brehmer et Gilles Louppe. « The frontier of simulation-based inference ». Proceedings of the National Academy of Sciences 117, no 48 (29 mai 2020) : 30055–62. http://dx.doi.org/10.1073/pnas.1912789117.
Texte intégralGouriéroux et Monfort. « Simulation Based Inference in Models with Heterogeneity ». Annales d'Économie et de Statistique, no 20/21 (1990) : 69. http://dx.doi.org/10.2307/20075807.
Texte intégralGhysels, Khalaf et Vodounou. « Simulation Based Inference in Moving Average Models ». Annales d'Économie et de Statistique, no 69 (2003) : 85. http://dx.doi.org/10.2307/20076364.
Texte intégralJasra, Ajay, David A. Stephens et Christopher C. Holmes. « On population-based simulation for static inference ». Statistics and Computing 17, no 3 (27 juillet 2007) : 263–79. http://dx.doi.org/10.1007/s11222-007-9028-9.
Texte intégralMcKinley, Trevelyan J., Joshua V. Ross, Rob Deardon et Alex R. Cook. « Simulation-based Bayesian inference for epidemic models ». Computational Statistics & ; Data Analysis 71 (mars 2014) : 434–47. http://dx.doi.org/10.1016/j.csda.2012.12.012.
Texte intégralTejero-Cantero, Alvaro, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro Gonçalves, David Greenberg et Jakob Macke. « sbi : A toolkit for simulation-based inference ». Journal of Open Source Software 5, no 52 (21 août 2020) : 2505. http://dx.doi.org/10.21105/joss.02505.
Texte intégralThèses sur le sujet "Simulation-Based Inference"
Rannestad, Bjarte. « Exact Statistical Inference in Nonhomogeneous Poisson Processes, based on Simulation ». Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10775.
Texte intégralWe present a general approach for Monte Carlo computation of conditional expectations of the form E[(T)|S = s] given a sufficient statistic S. The idea of the method was first introduced by Lillegård and Engen [4], and has been further developed by Lindqvist and Taraldsen [7, 8, 9]. If a certain pivotal structure is satised in our model, the simulation could be done by direct sampling from the conditional distribution, by a simple parameter adjustment of the original statistical model. In general it is shown by Lindqvist and Taraldsen [7, 8] that a weighted sampling scheme needs to be used. The method is in particular applied to the nonhomogeneous Poisson process, in order to develop exact goodness-of-fit tests for the null hypothesis that a set of observed failure times follow the NHPP of a specic parametric form. In addition exact confidence intervals for unknown parameters in the NHPP model are considered [6]. Different test statistics W=W(T) designed in order to reveal departure from the null model are presented [1, 10, 11]. By the method given in the following, the conditional expectation of these test statistics could be simulated in the absence of the pivotal structure mentioned above. This extends results given in [10, 11], and answers a question stated in [1]. We present a power comparison of 5 of the test statistics considered under the nullhypothesis that a set of observed failure times are from a NHPP with log linear intensity, under the alternative hypothesis of power law intensity. Finally a convergence comparison of the method presented here and an alternative approach of Gibbs sampling is given.
Rouillard, Louis. « Bridging Simulation-based Inference and Hierarchical Modeling : Applications in Neuroscience ». Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG024.
Texte intégralNeuroimaging investigates the brain's architecture and function using magnetic resonance (MRI). To make sense of the complex observed signal, Neuroscientists posit explanatory models, governed by interpretable parameters. This thesis tackles statistical inference : guessing which parameters could have yielded the signal through the model.Inference in Neuroimaging is complexified by at least three hurdles : a large dimensionality, a large uncertainty, and the hierarchcial structure of data. We look into variational inference (VI) as an optimization-based method to tackle this regime.Specifically, we conbine structured stochastic VI and normalizing flows (NFs) to design expressive yet scalable variational families. We apply those techniques in diffusion and functional MRI, on tasks including individual parcellation, microstructure inference and directional coupling estimation. Through these applications, we underline the interplay between the forward and reverse Kullback-Leibler (KL) divergences as complemen-tary tools for inference. We also demonstrate the ability of automatic VI (AVI) as a reliable and scalable inference method to tackle the challenges of model-driven Neuroscience
Khalaf, Lynda. « Simulation based finite and large sample inference methods in seemingly unrelated regressions and simultaneous equations ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0008/NQ38813.pdf.
Texte intégralFollestad, Turid. « Stochastic Modelling and Simulation Based Inference of Fish Population Dynamics and Spatial Variation in Disease Risk ». Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-41.
Texte intégralWe present a non-Gaussian and non-linear state-space model for the population dynamics of cod along the Norwegian Skagerak coast, embedded in the framework of a Bayesian hierarchical model. The model takes into account both process error, representing natural variability in the dynamics of a population, and observational error, reflecting the sampling process relating the observed data to true abundances. The data set on which our study is based, consists of samples of two juvenile age-groups of cod taken by beach seine hauls at a set of sample stations within several fjords along the coast. The age-structure population dynamics model, constituting the prior of the Bayesian model, is specified in terms of the recruitment process and the processes of survival for these two juvenile age-groups and the mature population, for which we have no data. The population dynamics is specified on abundances at the fjord level, and an explicit down-scaling from the fjord level to the level of the monitored stations is included in the likelihood, modelling the sampling process relating the observed counts to the underlying fjord abundances.
We take a sampling based approach to parameter estimation using Markov chain Monte Carlo methods. The properties of the model in terms of mixing and convergence of the MCMC algorithm and explored empirically on the basis of a simulated data set, and we show how the mixing properties can be improved by re-parameterisation. Estimation of the model parameters, and not the abundances, is the primary aim of the study, and we also propose an alternative approach to the estimation of the model parameters based on the marginal posterior distribution integrating over the abundances.
Based on the estimated model we illustrate how we can simulate the release of juvenile cod, imitating an experiment conducted in the early 20th century to resolve a controversy between a fisherman and a scientist who could not agree on the effect of releasing cod larvae on the mature abundance of cod. This controversy initiated the monitoring programme generating the data used in our study.
Fuentes, Antonio. « Proactive Decision Support Tools for National Park and Non-Traditional Agencies in Solving Traffic-Related Problems ». Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/88727.
Texte intégralDoctor of Philosophy
In this dissertation, a transportation system located in Jackson, Wyoming under the jurisdiction of the Grand Teton National Park and recognized as the Moose-Wilson Corridor is evaluated to identify transportation-related factors that influence its operational performance. The evaluation considers its unique prevalent conditions and takes into account future management strategies. Furthermore, emerging analytical strategies are implemented to identify and address transportation system operational concerns. Thus, in this dissertation, decision support tools for the evaluation of a unique system in a National Park are presented in four distinct manuscripts. The manuscripts cover traditional approaches that breakdown and evaluate traffic operations and identify mitigation strategies. Additionally, emerging strategies for the evaluation of data with machine learning approaches are implemented on GPS-tracks to determine vehicles stopping at park attractions. Lastly, an agent-based model is developed in a flexible platform to utilize previous findings and evaluate the Moose-Wilson corridor while considering future policy constraints and the unique natural interactions between visitors and prevalent ecological and wildlife.
Kazakov, Mikhaïl. « A Methodology of semi-automated software integration : an approach based on logical inference. Application to numerical simulation solutions of Open CASCADE ». INSA de Rouen, 2004. http://www.theses.fr/2004ISAM0001.
Texte intégralCho, B. « Control of a hybrid electric vehicle with predictive journey estimation ». Thesis, Cranfield University, 2008. http://hdl.handle.net/1826/2589.
Texte intégralDominicy, Yves. « Quantile-based inference and estimation of heavy-tailed distributions ». Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209311.
Texte intégralThe first chapter introduces a quantile- and simulation-based estimation method, which we call the Method of Simulated Quantiles, or simply MSQ. Since it is based on quantiles, it is a moment-free approach. And since it is based on simulations, we do not need closed form expressions of any function that represents the probability law of the process. Thus, it is useful in case the probability density functions has no closed form or/and moments do not exist. It is based on a vector of functions of quantiles. The principle consists in matching functions of theoretical quantiles, which depend on the parameters of the assumed probability law, with those of empirical quantiles, which depend on the data. Since the theoretical functions of quantiles may not have a closed form expression, we rely on simulations.
The second chapter deals with the estimation of the parameters of elliptical distributions by means of a multivariate extension of MSQ. In this chapter we propose inference for vast dimensional elliptical distributions. Estimation is based on quantiles, which always exist regardless of the thickness of the tails, and testing is based on the geometry of the elliptical family. The multivariate extension of MSQ faces the difficulty of constructing a function of quantiles that is informative about the covariation parameters. We show that the interquartile range of a projection of pairwise random variables onto the 45 degree line is very informative about the covariation.
The third chapter consists in constructing a multivariate tail index estimator. In the univariate case, the most popular estimator for the tail exponent is the Hill estimator introduced by Bruce Hill in 1975. The aim of this chapter is to propose an estimator of the tail index in a multivariate context; more precisely, in the case of regularly varying elliptical distributions. Since, for univariate random variables, our estimator boils down to the Hill estimator, we name it after Bruce Hill. Our estimator is based on the distance between an elliptical probability contour and the exceedance observations.
Finally, the fourth chapter investigates the asymptotic behaviour of the marginal sample quantiles for p-dimensional stationary processes and we obtain the asymptotic normality of the empirical quantile vector. We assume that the processes are S-mixing, a recently introduced and widely applicable notion of dependence. A remarkable property of S-mixing is the fact that it doesn't require any higher order moment assumptions to be verified. Since we are interested in quantiles and processes that are probably heavy-tailed, this is of particular interest.
Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished
Toft, Albin. « Particle-based Parameter Inference in Stochastic Volatility Models : Batch vs. Online ». Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252313.
Texte intégralDetta examensarbetefokuserar på att jämföra en online och offline parameter-skattare i stokastiskavolatilitets modeller. De två parameter-skattarna som jämförs är båda baseradepå PaRIS-algoritmen. Genom att modellera en stokastisk volatilitets-model somen dold Markov kedja, kunde partikelbaserade parameter-skattare användas föratt uppskatta de okända parametrarna i modellen. Resultaten presenterade idetta examensarbete tyder på att online-implementationen av PaRIS-algorimen kanses som det bästa alternativet, jämfört med offline-implementationen.Resultaten är dock inte helt övertygande, och ytterligare forskning inomområdet
Wang, Shiwei. « Motion Control for Intelligent Ground Vehicles Based on the Selection of Paths Using Fuzzy Inference ». Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/725.
Texte intégralLivres sur le sujet "Simulation-Based Inference"
Dufour, Jean-Marie. Simulation based finite and large sample inference methods in multivariate regressions and seemingly unrelated regressions. Bristol : University of Bristol, Department of Economics, 1999.
Trouver le texte intégralS, Teichrow Jon, University of Houston--Clear Lake. Research Institute for Computing and Information Systems. et Lyndon B. Johnson Space Center. Information Technology Division., dir. Real-time fuzzy inference based robot path planning : Final report. [Clear Lake City, Tex.?] : Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1990.
Trouver le texte intégralMariano, Roberto, Til Schuermann et Melvyn J. Weeks, dir. Simulation-based Inference in Econometrics. Cambridge University Press, 2000. http://dx.doi.org/10.1017/cbo9780511751981.
Texte intégralSchuermann, Til, Roberto Mariano et Melvyn J. Weeks. Simulation-Based Inference in Econometrics : Methods and Applications. Cambridge University Press, 2010.
Trouver le texte intégralSimulation-based Inference in Econometrics : Methods and Applications. Cambridge University Press, 2008.
Trouver le texte intégral(Editor), Roberto Mariano, Til Schuermann (Editor) et Melvyn J. Weeks (Editor), dir. Simulation-based Inference in Econometrics : Methods and Applications. Cambridge University Press, 2000.
Trouver le texte intégralLevin, Ines, et Betsy Sinclair. Causal Inference with Complex Survey Designs. Sous la direction de Lonna Rae Atkeson et R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.4.
Texte intégralQuintana, José Mario, Carlos Carvalho, James Scott et Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility : The Credit Crisis of 2007–2008. Sous la direction de Anthony O'Hagan et Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.
Texte intégralReal-time fuzzy inference based robot path planning : Final report. [Clear Lake City, Tex.?] : Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1990.
Trouver le texte intégralBao, Yun, Carl Chiarella et Boda Kang. Particle Filters for Markov-Switching Stochastic Volatility Models. Sous la direction de Shu-Heng Chen, Mak Kaboudan et Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.9.
Texte intégralChapitres de livres sur le sujet "Simulation-Based Inference"
Jávor, András. « Knowledge Based Inference Controlled Logic Simulation ». Dans Advances in Simulation, 397–405. New York, NY : Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-6389-7_80.
Texte intégralYurrita, Mireia, Arnaud Grignard, Luis Alonso et Kent Larson. « Real-Time Inference of Urban Metrics Applying Machine Learning to an Agent-Based Model Coupling Mobility Mode and Housing Choice ». Dans Multi-Agent-Based Simulation XXII, 125–38. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94548-0_10.
Texte intégralAyusuk, Apiwat, et Songsak Sriboonchitta. « Copula Based Volatility Models and Extreme Value Theory for Portfolio Simulation with an Application to Asian Stock Markets ». Dans Causal Inference in Econometrics, 279–93. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27284-9_17.
Texte intégralCabarcos, M., R. P. Otero et S. G. Pose. « Efficient Concurrent Simulation of DEVS Systems Based on Concurrent Inference ». Dans Computer Aided Systems Theory - EUROCAST’99, 307–18. Berlin, Heidelberg : Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10720123_28.
Texte intégralMøller, Jesper, et Rasmus P. Waagepetersen. « An Introduction to Simulation-Based Inference for Spatial Point Processes ». Dans Spatial Statistics and Computational Methods, 143–98. New York, NY : Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21811-3_4.
Texte intégralBen Abdessalem, Anis. « Structural Model Updating and Model Selection : Bayesian Inference Approach Based on Simulation ». Dans Lecture Notes in Civil Engineering, 223–33. Cham : Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57224-1_22.
Texte intégralLi, Xiao, et Xiaoping Chen. « Fuzzy Inference Based Forecasting in Soccer Simulation 2D, the RoboCup 2015 Soccer Simulation 2D League Champion Team ». Dans RoboCup 2015 : Robot World Cup XIX, 144–52. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-29339-4_12.
Texte intégralKim, Mingoo, et Taeseok Jin. « Simulation of Mobile Robot Navigation Using the Distributed Control Command Based Fuzzy Inference ». Dans Intelligent Robotics and Applications, 457–66. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13966-1_45.
Texte intégralHerbach, Ulysse. « Harissa : Stochastic Simulation and Inference of Gene Regulatory Networks Based on Transcriptional Bursting ». Dans Computational Methods in Systems Biology, 97–105. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42697-1_7.
Texte intégralFlores-Parra, Josue-Miguel, Manuel Castañón-Puga, Carelia Gaxiola-Pacheco, Luis-Enrique Palafox-Maestre, Ricardo Rosales et Alfredo Tirado-Ramos. « A Fuzzy Inference System and Data Mining Toolkit for Agent-Based Simulation in NetLogo ». Dans Computer Science and Engineering—Theory and Applications, 127–49. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74060-7_7.
Texte intégralActes de conférences sur le sujet "Simulation-Based Inference"
Nevin, Becky. « Simulation-based Inference Cosmology from Strong Lensing ». Dans Simulation-based Inference Cosmology from Strong Lensing. US DOE, 2024. http://dx.doi.org/10.2172/2282440.
Texte intégralTamargo-Arizmendi, Marcos. « Simulation Based Inference with Domain Adaptation for Strong Gravitational Lensing ». Dans Simulation Based Inference with Domain Adaptation for Strong Gravitational Lensing. US DOE, 2023. http://dx.doi.org/10.2172/2246937.
Texte intégralJarugula, Sreevani, Brian Nord, Jason Poh, Aleksandra Ciprijanovic et Becky Nevin. « Cosmology constraints from Strong Gravitational Lensing using Simulation-Based Inference ». Dans Cosmology constraints from Strong Gravitational Lensing using Simulation-Based Inference. US DOE, 2023. http://dx.doi.org/10.2172/2246728.
Texte intégralPoh, Jason, Ashwin Samudre, Aleksandra Ciprijanovic, Brian Nord, Gourav Khullar, Dimitrios Tanoglidis et Joshua Frieman. « Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference ». Dans Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference. US DOE, 2023. http://dx.doi.org/10.2172/1958791.
Texte intégralDunham, Bruce. « Exploring Simulation-Based Inference in a High School Course ». Dans Bridging the Gap : Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t14a3.
Texte intégralKulkarni, Sourabh, Alexander Tsyplikhin, Mario Michael Krell et Csaba Andras Moritz. « Accelerating Simulation-based Inference with Emerging AI Hardware ». Dans 2020 International Conference on Rebooting Computing (ICRC). IEEE, 2020. http://dx.doi.org/10.1109/icrc2020.2020.00003.
Texte intégralDong, Ming, Jianzhong Cha et Mingcheng E. « Knowledge-Based Integrated Manufacturing Flexible Simulation System ». Dans ASME 1996 Design Engineering Technical Conferences and Computers in Engineering Conference. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-detc/dac-1041.
Texte intégralYun-Jie, Ji, Chen Wen-Qi et He Ling. « Risk Identification and Simulation Based on the Bayesian Inference ». Dans 2018 4th Annual International Conference on Network and Information Systems for Computers (ICNISC). IEEE, 2018. http://dx.doi.org/10.1109/icnisc.2018.00089.
Texte intégralBoyali, Ali, Simon Thompson et David Robert Wong. « Identification of Vehicle Dynamics Parameters Using Simulation-based Inference ». Dans 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops). IEEE, 2021. http://dx.doi.org/10.1109/ivworkshops54471.2021.9669252.
Texte intégralIKIDID, Abdelouafi, et El Fazziki Abdelaziz. « Multi-Agent and Fuzzy Inference Based Framework for Urban Traffic Simulation ». Dans 2019 Fourth International Conference on Systems of Collaboration Big Data, Internet of Things & Security ( SysCoBIoTS). IEEE, 2019. http://dx.doi.org/10.1109/syscobiots48768.2019.9028016.
Texte intégralRapports d'organisations sur le sujet "Simulation-Based Inference"
Chen, Zhenzhen. Model-based analysis in survey : an application in analytic inference and a simulation in Small Area Estimation. Ames (Iowa) : Iowa State University, janvier 2019. http://dx.doi.org/10.31274/cc-20240624-1010.
Texte intégralPaule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER et Nicolo Ferrari. PRELUDE Roadmap for Building Renovation : set of rules for renovation actions to optimize building energy performance. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541614638.
Texte intégralStewart, Jonathan, Grace Parker, Joseph Harmon, Gail Atkinson, David Boore, Robert Darragh, Walter Silva et Youssef Hashash. Expert Panel Recommendations for Ergodic Site Amplification in Central and Eastern North America. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, mars 2017. http://dx.doi.org/10.55461/tzsy8988.
Texte intégralTsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora et Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, juin 2021. http://dx.doi.org/10.31812/123456789/4370.
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