Relevant bibliographies by topics / Sequential Monte Carlo methods

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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 'Sequential Monte Carlo methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2022

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Journal articles on the topic "Sequential Monte Carlo methods"

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Diaconis, Persi. "Sequential Monte Carlo Methods in Practice." Journal of the American Statistical Association 98, no. 462 (June 2003): 496–97. http://dx.doi.org/10.1198/jasa.2003.s282.

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Sarkar, Pradipta. "Sequential Monte Carlo Methods in Practice." Technometrics 45, no. 1 (February 2003): 106. http://dx.doi.org/10.1198/tech.2003.s23.

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Everitt, Richard G., Richard Culliford, Felipe Medina-Aguayo, and Daniel J. Wilson. "Sequential Monte Carlo with transformations." Statistics and Computing 30, no. 3 (November 17, 2019): 663–76. http://dx.doi.org/10.1007/s11222-019-09903-y.

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AbstractThis paper examines methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. For this, we use sequential Monte Carlo samplers, introducing the innovation of using deterministic transformations to move particles effectively between target distributions with different dimensions. This approach, combined with adaptive methods, yields an extremely flexible and general algorithm for Bayesian model comparison that is suitable for use in applications where the acceptance rate in reversible jump Markov chain Monte Carlo is low. We use this approach on model comparison for mixture models, and for inferring coalescent trees sequentially, as data arrives.
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Akkam Veettil, Dilshad R., and Kit Clark. "Bayesian Geosteering Using Sequential Monte Carlo Methods." Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 61, no. 1 (February 1, 2020): 99–111. http://dx.doi.org/10.30632/pjv61n1-2020a4.

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Jasra, Ajay, and Pierre Del Moral. "Sequential Monte Carlo Methods for Option Pricing." Stochastic Analysis and Applications 29, no. 2 (February 25, 2011): 292–316. http://dx.doi.org/10.1080/07362994.2011.548993.

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Jasra, Ajay, and Arnaud Doucet. "Sequential Monte Carlo methods for diffusion processes." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 465, no. 2112 (September 11, 2009): 3709–27. http://dx.doi.org/10.1098/rspa.2009.0206.

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In this paper, we show how to use sequential Monte Carlo methods to compute expectations of functionals of diffusions at a given time and the gradients of these quantities w.r.t. the initial condition of the process. In some cases, via the exact simulation of the diffusion, there is no time discretization error, otherwise the methods use Euler discretization. We illustrate our approach on both high- and low-dimensional problems from optimal control and establish that our approach substantially outperforms standard Monte Carlo methods typically adopted in the literature. The methods developed here are appropriate for solving a certain class of partial differential equations as well as for option pricing and hedging.
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Kemp, Freda. "An Introduction to Sequential Monte Carlo Methods." Journal of the Royal Statistical Society: Series D (The Statistician) 52, no. 4 (December 2003): 694–95. http://dx.doi.org/10.1046/j.1467-9884.2003.t01-6-00383_8.x.

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Liu, Jun S., and Rong Chen. "Sequential Monte Carlo Methods for Dynamic Systems." Journal of the American Statistical Association 93, no. 443 (September 1998): 1032–44. http://dx.doi.org/10.1080/01621459.1998.10473765.

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Finke, Axel, Arnaud Doucet, and Adam M. Johansen. "Limit theorems for sequential MCMC methods." Advances in Applied Probability 52, no. 2 (June 2020): 377–403. http://dx.doi.org/10.1017/apr.2020.9.

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AbstractBoth sequential Monte Carlo (SMC) methods (a.k.a. ‘particle filters’) and sequential Markov chain Monte Carlo (sequential MCMC) methods constitute classes of algorithms which can be used to approximate expectations with respect to (a sequence of) probability distributions and their normalising constants. While SMC methods sample particles conditionally independently at each time step, sequential MCMC methods sample particles according to a Markov chain Monte Carlo (MCMC) kernel. Introduced over twenty years ago in [6], sequential MCMC methods have attracted renewed interest recently as they empirically outperform SMC methods in some applications. We establish an $\mathbb{L}_r$ -inequality (which implies a strong law of large numbers) and a central limit theorem for sequential MCMC methods and provide conditions under which errors can be controlled uniformly in time. In the context of state-space models, we also provide conditions under which sequential MCMC methods can indeed outperform standard SMC methods in terms of asymptotic variance of the corresponding Monte Carlo estimators.
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Eberle, Andreas, and Carlo Marinelli. "Stability of sequential Markov Chain Monte Carlo methods." ESAIM: Proceedings 19 (2007): 22–31. http://dx.doi.org/10.1051/proc:071905.

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Dissertations / Theses on the topic "Sequential Monte Carlo methods"

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Fearnhead, Paul. "Sequential Monte Carlo methods in filter theory." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299043.

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Punskaya, Elena. "Sequential Monte Carlo methods for digital communications." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620013.

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Henderson, Donna. "Sequential Monte Carlo methods for demographic inference." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:a3516e76-ac95-4efc-9d57-53092ca4c8f3.

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Patterns of mutations in the DNA of modern-day individuals have been shaped by the demographic history of our ancestors. Inferring the demographic history from these patterns is a challenging problem due to complex dependencies along the genome. Several recent methods have adopted McVean's sequentially Markovian coalescent (SMC') to model these dependencies. However, these methods involve simplifying assumptions that preclude the inference of rates of migration between populations. We have developed the first method to infer directional migration rates as a function of time. To do this, we employ sequential Monte Carlo (SMC) methods, also known as particle filters, to infer parameters in the SMC' model. To improve the sampling from the state space of SMC' we have developed a sophisticated sampling technique that shows better performance than the standard bootstrap filter. We apply our algorithm, SMC2, to Neanderthal data and are able to infer the time and extent of migration from the Vindija Neanderthal population into Europeans. With the large volume of sequencing data being produced from diverse populations, both modern and ancient, there is high demand for methods to interrogate this data. SMC2 provides a flexible algorithm, which can be modified to suit many data applications. For instance, we show that our method performs well when the phasing of the samples is unknown, which is often the case in practice. The long runtime of SMC2 is the main limiting factor in the adoption of the method. We have started to explore ways to improve the runtime, by developing an adaptive online expectation maximisation (EM) procedure.
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Li, Jun Feng. "Sequential Monte Carlo methods for multiple target tracking." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612269.

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Dias, Stiven Schwanz. "Collaborative emitter tracking using distributed sequential Monte Carlo methods." Instituto Tecnológico de Aeronáutica, 2014. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=3137.

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We introduce in this Thesis several particle filter (PF) solutions to the problem of collaborative emitter tracking. In the studied scenario, multiple agents with sensing, processing and communication capabilities passively collect received-signal-strength (RSS) measurements of the same signal originating from a non-cooperative emitter and collaborate to estimate its hidden state. Assuming unknown sensor noise variances, we derive an exact decentralized implementation of the optimal centralized PF solution for this problem in a fully connected network. Next, assuming local internode communication only, we derive two fully distributed consensus-based solutions to the problem using respectively average consensus iterations and a novel ordered minimum consensus approach which allow us to reproduce the exact centralized solution in a finite number of consensus iterations. In the sequel, to reduce the communication cost, we derive a suboptimal tracker which employs suitable parametric approximations to summarize messages that are broadcast over the network. Moreover, to further reduce communication and processing requirements, we introduce a non-iterative tracker based on random information dissemination which is suited for online applications. We derive the proposed random exchange diffusion PF (ReDif-PF) assuming both that observation model parameters are perfectly known and that the emitter is always present. We extend then the ReDif-PF tracker to operate in scenarios with unknown sensor noise variances and propose the Rao-Blackwellized (RB) ReDif-PF. Finally, we introduce the random exchange diffusion Bernoulli filter (RndEx-BF) which enables the network of collaborative RSS sensors to jointly detect and track the emitter within the surveillance space.
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Creal, Drew D. "Essays in sequential Monte Carlo methods for economics and finance /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/7444.

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Petrov, Nikolay. "Sequential Monte Carlo methods for extended and group object tracking." Thesis, Lancaster University, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658087.

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This dissertation deals with the challenging tasks of real-time extended and group object tracking. The problems are formulated as joint parameter and state estimation of dynamic systems. The solutions proposed are formulated within a general nonlinear framework and are based on the Sequential Monte Carlo (SMC) method, also known as Particle Filtering (PF) method. Eour different solutions are proposed for the extended object tracking problem. The first two are based on border parametrisation of the visible surface of the extended object. The likelihood functions are derived for two different scenarios - one without clutter in the measurements and another one in the presence of clutter. In the third approach the kernel density estimation technique is utilised to approximate the joint posterior density of the target dynamic state and static size parameters. The forth proposed approach solves the extended object tracking problem based on the recently emerged SMC method combined with interval analysis , called Box Particle Filter (Box P F). Simulation results for all of the developed algorithms show accurate online tracking, with very good estimates both for the target kinematic states and for the parameters of the target extent. In addition, the performance of the Box PF and the border parametrised PF is validated utilising real measurements from laser range scanners obtained within a prototype security system replicating an airport corridor.
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Johansen, Adam Michael. "Some non-standard sequential Monte Carlo methods and their applications." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612877.

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Arnold, Andrea. "Sequential Monte Carlo Parameter Estimation for Differential Equations." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1396617699.

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Brasnett, Paul. "Sequential Monte-Carlo methods for object tracking and replacement in video." Thesis, University of Bristol, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442196.

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Books on the topic "Sequential Monte Carlo methods"

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Doucet, Arnaud, Nando Freitas, and Neil Gordon, eds. Sequential Monte Carlo Methods in Practice. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9.

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A, Whitlock Paula, and Wiley online library, eds. Monte Carlo methods. 2nd ed. Weinheim: Wiley-Blackwell, 2008.

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Kalos, Malvin H. Monte Carlo methods. New York: J. Wiley & Sons, 1986.

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Kalos, Malvin H., and Paula A. Whitlock, eds. Monte Carlo Methods. Weinheim, Germany: Wiley-VCH Verlag GmbH, 1986. http://dx.doi.org/10.1002/9783527617395.

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Sabelfeld, Karl K. Monte Carlo Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-75977-2.

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Barbu, Adrian, and Song-Chun Zhu. Monte Carlo Methods. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2971-5.

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A, Whitlock Paula, ed. Monte Carlo methods. New York: J. Wiley & Sons, 1986.

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Tuffin, Bruno, and Pierre L'Ecuyer, eds. Monte Carlo and Quasi-Monte Carlo Methods. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43465-6.

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Cools, Ronald, and Dirk Nuyens, eds. Monte Carlo and Quasi-Monte Carlo Methods. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33507-0.

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Owen, Art B., and Peter W. Glynn, eds. Monte Carlo and Quasi-Monte Carlo Methods. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91436-7.

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Book chapters on the topic "Sequential Monte Carlo methods"

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Barbu, Adrian, and Song-Chun Zhu. "Sequential Monte Carlo." In Monte Carlo Methods, 19–48. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2971-5_2.

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Petris, Giovanni, Sonia Petrone, and Patrizia Campagnoli. "Sequential Monte Carlo methods." In Dynamic Linear Models with R, 207–29. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/b135794_5.

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Doucet, Arnaud, and Anthony Lee. "Sequential Monte Carlo Methods." In Handbook of Graphical Models, 165–88. Boca Raton, Florida : CRC Press, c2019.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429463976-7.

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Kong, Rong, and Jerome Spanier. "Sequential Correlated Sampling Methods for Some Transport Problems." In Monte-Carlo and Quasi-Monte Carlo Methods 1998, 238–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59657-5_16.

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Giraud, François, and Pierre Del Moral. "On the Convergence of Quantum and Sequential Monte Carlo Methods." In Monte Carlo and Quasi-Monte Carlo Methods 2012, 385–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41095-6_17.

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Kong, Rong, and Jerome Spanier. "Error Analysis of Sequential Monte Carlo Methods for Transport Problems." In Monte-Carlo and Quasi-Monte Carlo Methods 1998, 252–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59657-5_17.

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Spanier, Jerome, and Liming Li. "General Sequential Sampling Techniques for Monte Carlo Simulations: Part I—Matrix Problems." In Monte Carlo and Quasi-Monte Carlo Methods 1996, 382–97. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1690-2_27.

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Doucet, Arnaud, Nando Freitas, and Neil Gordon. "An Introduction to Sequential Monte Carlo Methods." In Sequential Monte Carlo Methods in Practice, 3–14. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9_1.

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Freitas, N., C. Andrieu, P. Højen-Sørensen, M. Niranjan, and A. Gee. "Sequential Monte Carlo Methods for Neural Networks." In Sequential Monte Carlo Methods in Practice, 359–79. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9_17.

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Andrieu, Christophe, Arnaud Doucet, and Elena Punskaya. "Sequential Monte Carlo Methods for Optimal Filtering." In Sequential Monte Carlo Methods in Practice, 79–95. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9_4.

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Conference papers on the topic "Sequential Monte Carlo methods"

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Hong, Inpyo, Daeyoung Kim, and Daehong Kim. "Restrained Resampling for Sequential Monte Carlo Methods." In Signal and Image Processing. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.786-083.

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Urteaga, Inigo, Monica F. Bugallo, and Petar M. Djuric. "Sequential Monte Carlo methods under model uncertainty." In 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016. http://dx.doi.org/10.1109/ssp.2016.7551747.

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Sijivac, D., S. Nikolovski, and Z. Kovac. "Distribution Network Restoration Using Sequential Monte Carlo Approach." In 2006 International Conference on Probabilistic Methods Applied to Power Systems. IEEE, 2006. http://dx.doi.org/10.1109/pmaps.2006.360267.

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Urfalioglu, Onay, Ercan E. Kuruoglu, and A. Enis Cetin. "Superimposed Event Detection by Sequential Monte Carlo Methods." In 2007 IEEE 15th Signal Processing and Communications Applications. IEEE, 2007. http://dx.doi.org/10.1109/siu.2007.4298796.

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Hanif, Ayub, and Robert E. Smith. "Generation path-switching in sequential Monte-Carlo methods." In 2012 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2012. http://dx.doi.org/10.1109/cec.2012.6256581.

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Li, Xinrong, and Jue Yang. "Sequential Monte Carlo Methods for Collaborative Multi-Sensor Tracking." In MILCOM 2007 - IEEE Military Communications Conference. IEEE, 2007. http://dx.doi.org/10.1109/milcom.2007.4454958.

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Jaward, M. H., D. Bull, and N. Canagarajah. "Distributed Tracking with Sequential Monte Carlo Methods for Manoeuvrable Sensors." In 2006 IEEE Nonlinear Statistical Signal Processing Workshop. IEEE, 2006. http://dx.doi.org/10.1109/nsspw.2006.4378832.

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Li, J., W. Ng, S. Godsill, and J. Vermaak. "Online multitarget detection and tracking using sequential Monte Carlo methods." In 2005 7th International Conference on Information Fusion. IEEE, 2005. http://dx.doi.org/10.1109/icif.2005.1591844.

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Evers, Christine, James R. Hopgood, and Judith Bell. "Blind speech dereverberation using batch and sequential Monte Carlo methods." In 2008 IEEE International Symposium on Circuits and Systems - ISCAS 2008. IEEE, 2008. http://dx.doi.org/10.1109/iscas.2008.4542145.

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Zhang, Dongqing, Xuanxi Ning, Xueni Liu, and Hongwei Ma. "Prediction in hidden Markov Models using sequential Monte Carlo methods." In 2007 IEEE International Conference on Grey Systems and Intelligent Services. IEEE, 2007. http://dx.doi.org/10.1109/gsis.2007.4443368.

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Reports on the topic "Sequential Monte Carlo methods"

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Acton, Scott T., and Bing Li. A Sequential Monte Carlo Method for Real-time Tracking of Multiple Targets. Fort Belvoir, VA: Defense Technical Information Center, May 2010. http://dx.doi.org/10.21236/ada532576.

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Vogel, Thomas. Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1148317.

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Herbst, Edward, and Frank Schorfheide. Sequential Monte Carlo Sampling for DSGE Models. Cambridge, MA: National Bureau of Economic Research, June 2013. http://dx.doi.org/10.3386/w19152.

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Hungerford, Aimee L. (U) Introduction to Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), March 2017. http://dx.doi.org/10.2172/1351179.

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Brown, Forrest B. Advanced Computational Methods for Monte Carlo Calculations. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1417155.

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Caflisch, Russel E. Rarefied Gas Dynamics and Monte Carlo Methods. Fort Belvoir, VA: Defense Technical Information Center, May 1995. http://dx.doi.org/10.21236/ada295375.

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Creutz, M. Lattice gauge theory and Monte Carlo methods. Office of Scientific and Technical Information (OSTI), November 1988. http://dx.doi.org/10.2172/6530895.

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Jerome Spanier. Third International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC98). Office of Scientific and Technical Information (OSTI), March 1999. http://dx.doi.org/10.2172/761782.

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Owen, Richard Kent. Quantum Monte Carlo methods and lithium cluster properties. Office of Scientific and Technical Information (OSTI), December 1990. http://dx.doi.org/10.2172/10180548.

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Meadors, Grant. Forecasting the Solar Wind with Sequential Monte Carlo Assimilation of Satellite Data [Slides]. Office of Scientific and Technical Information (OSTI), March 2021. http://dx.doi.org/10.2172/1770082.

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