Academic literature on the topic 'Simulation-Based Inference'
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Journal articles on the topic "Simulation-Based Inference":
Gourieroux, Christian, and Alain Monfort. "Simulation-based inference." Journal of Econometrics 59, no. 1-2 (September 1993): 5–33. http://dx.doi.org/10.1016/0304-4076(93)90037-6.
Vogelsang, T. J. "Simulation-Based Inference in Econometrics." Journal of the American Statistical Association 97, no. 458 (June 2002): 657. http://dx.doi.org/10.1198/jasa.2002.s478.
Brehmer, Johann. "Simulation-based inference in particle physics." Nature Reviews Physics 3, no. 5 (March 22, 2021): 305. http://dx.doi.org/10.1038/s42254-021-00305-6.
Sheu, Ching-fan, and Suzanne L. O’Curry. "Simulation-based bayesian inference using BUGS." Behavior Research Methods, Instruments, & Computers 30, no. 2 (June 1998): 232–37. http://dx.doi.org/10.3758/bf03200649.
Cranmer, Kyle, Johann Brehmer, and Gilles Louppe. "The frontier of simulation-based inference." Proceedings of the National Academy of Sciences 117, no. 48 (May 29, 2020): 30055–62. http://dx.doi.org/10.1073/pnas.1912789117.
Gouriéroux and 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.
Ghysels, Khalaf, and 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.
Jasra, Ajay, David A. Stephens, and Christopher C. Holmes. "On population-based simulation for static inference." Statistics and Computing 17, no. 3 (July 27, 2007): 263–79. http://dx.doi.org/10.1007/s11222-007-9028-9.
McKinley, Trevelyan J., Joshua V. Ross, Rob Deardon, and Alex R. Cook. "Simulation-based Bayesian inference for epidemic models." Computational Statistics & Data Analysis 71 (March 2014): 434–47. http://dx.doi.org/10.1016/j.csda.2012.12.012.
Tejero-Cantero, Alvaro, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro Gonçalves, David Greenberg, and Jakob Macke. "sbi: A toolkit for simulation-based inference." Journal of Open Source Software 5, no. 52 (August 21, 2020): 2505. http://dx.doi.org/10.21105/joss.02505.
Dissertations / Theses on the topic "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.
We 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.
Neuroimaging 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.
Follestad, 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.
We 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.
Doctor 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.
Cho, B. "Control of a hybrid electric vehicle with predictive journey estimation." Thesis, Cranfield University, 2008. http://hdl.handle.net/1826/2589.
Dominicy, 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.
The 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.
Detta 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.
Books on the topic "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.
S, Teichrow Jon, University of Houston--Clear Lake. Research Institute for Computing and Information Systems., and Lyndon B. Johnson Space Center. Information Technology Division., eds. 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.
Mariano, Roberto, Til Schuermann, and Melvyn J. Weeks, eds. Simulation-based Inference in Econometrics. Cambridge University Press, 2000. http://dx.doi.org/10.1017/cbo9780511751981.
Schuermann, Til, Roberto Mariano, and Melvyn J. Weeks. Simulation-Based Inference in Econometrics: Methods and Applications. Cambridge University Press, 2010.
Simulation-based Inference in Econometrics: Methods and Applications. Cambridge University Press, 2008.
(Editor), Roberto Mariano, Til Schuermann (Editor), and Melvyn J. Weeks (Editor), eds. Simulation-based Inference in Econometrics: Methods and Applications. Cambridge University Press, 2000.
Levin, Ines, and Betsy Sinclair. Causal Inference with Complex Survey Designs. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.4.
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.
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.
Bao, Yun, Carl Chiarella, and Boda Kang. Particle Filters for Markov-Switching Stochastic Volatility Models. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.9.
Book chapters on the topic "Simulation-Based Inference":
Jávor, András. "Knowledge Based Inference Controlled Logic Simulation." In Advances in Simulation, 397–405. New York, NY: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-6389-7_80.
Yurrita, Mireia, Arnaud Grignard, Luis Alonso, and Kent Larson. "Real-Time Inference of Urban Metrics Applying Machine Learning to an Agent-Based Model Coupling Mobility Mode and Housing Choice." In Multi-Agent-Based Simulation XXII, 125–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94548-0_10.
Ayusuk, Apiwat, and Songsak Sriboonchitta. "Copula Based Volatility Models and Extreme Value Theory for Portfolio Simulation with an Application to Asian Stock Markets." In Causal Inference in Econometrics, 279–93. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27284-9_17.
Cabarcos, M., R. P. Otero, and S. G. Pose. "Efficient Concurrent Simulation of DEVS Systems Based on Concurrent Inference." In Computer Aided Systems Theory - EUROCAST’99, 307–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10720123_28.
Møller, Jesper, and Rasmus P. Waagepetersen. "An Introduction to Simulation-Based Inference for Spatial Point Processes." In 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.
Ben Abdessalem, Anis. "Structural Model Updating and Model Selection: Bayesian Inference Approach Based on Simulation." In Lecture Notes in Civil Engineering, 223–33. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57224-1_22.
Li, Xiao, and Xiaoping Chen. "Fuzzy Inference Based Forecasting in Soccer Simulation 2D, the RoboCup 2015 Soccer Simulation 2D League Champion Team." In 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.
Kim, Mingoo, and Taeseok Jin. "Simulation of Mobile Robot Navigation Using the Distributed Control Command Based Fuzzy Inference." In Intelligent Robotics and Applications, 457–66. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13966-1_45.
Herbach, Ulysse. "Harissa: Stochastic Simulation and Inference of Gene Regulatory Networks Based on Transcriptional Bursting." In Computational Methods in Systems Biology, 97–105. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42697-1_7.
Flores-Parra, Josue-Miguel, Manuel Castañón-Puga, Carelia Gaxiola-Pacheco, Luis-Enrique Palafox-Maestre, Ricardo Rosales, and Alfredo Tirado-Ramos. "A Fuzzy Inference System and Data Mining Toolkit for Agent-Based Simulation in NetLogo." In 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.
Conference papers on the topic "Simulation-Based Inference":
Nevin, Becky. "Simulation-based Inference Cosmology from Strong Lensing." In Simulation-based Inference Cosmology from Strong Lensing. US DOE, 2024. http://dx.doi.org/10.2172/2282440.
Tamargo-Arizmendi, Marcos. "Simulation Based Inference with Domain Adaptation for Strong Gravitational Lensing." In Simulation Based Inference with Domain Adaptation for Strong Gravitational Lensing. US DOE, 2023. http://dx.doi.org/10.2172/2246937.
Jarugula, Sreevani, Brian Nord, Jason Poh, Aleksandra Ciprijanovic, and Becky Nevin. "Cosmology constraints from Strong Gravitational Lensing using Simulation-Based Inference." In Cosmology constraints from Strong Gravitational Lensing using Simulation-Based Inference. US DOE, 2023. http://dx.doi.org/10.2172/2246728.
Poh, Jason, Ashwin Samudre, Aleksandra Ciprijanovic, Brian Nord, Gourav Khullar, Dimitrios Tanoglidis, and Joshua Frieman. "Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference." In Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference. US DOE, 2023. http://dx.doi.org/10.2172/1958791.
Dunham, Bruce. "Exploring Simulation-Based Inference in a High School Course." In 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.
Kulkarni, Sourabh, Alexander Tsyplikhin, Mario Michael Krell, and Csaba Andras Moritz. "Accelerating Simulation-based Inference with Emerging AI Hardware." In 2020 International Conference on Rebooting Computing (ICRC). IEEE, 2020. http://dx.doi.org/10.1109/icrc2020.2020.00003.
Dong, Ming, Jianzhong Cha, and Mingcheng E. "Knowledge-Based Integrated Manufacturing Flexible Simulation System." In 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.
Yun-Jie, Ji, Chen Wen-Qi, and He Ling. "Risk Identification and Simulation Based on the Bayesian Inference." In 2018 4th Annual International Conference on Network and Information Systems for Computers (ICNISC). IEEE, 2018. http://dx.doi.org/10.1109/icnisc.2018.00089.
Boyali, Ali, Simon Thompson, and David Robert Wong. "Identification of Vehicle Dynamics Parameters Using Simulation-based Inference." In 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops). IEEE, 2021. http://dx.doi.org/10.1109/ivworkshops54471.2021.9669252.
IKIDID, Abdelouafi, and El Fazziki Abdelaziz. "Multi-Agent and Fuzzy Inference Based Framework for Urban Traffic Simulation." In 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.
Reports on the topic "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, January 2019. http://dx.doi.org/10.31274/cc-20240624-1010.
Paule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER, and 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.
Stewart, Jonathan, Grace Parker, Joseph Harmon, Gail Atkinson, David Boore, Robert Darragh, Walter Silva, and 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, March 2017. http://dx.doi.org/10.55461/tzsy8988.
Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, June 2021. http://dx.doi.org/10.31812/123456789/4370.