Literatura académica sobre el tema "Sequential Monte Carlo (SMC) method"
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Artículos de revistas sobre el tema "Sequential Monte Carlo (SMC) method"
Wang, Liangliang, Shijia Wang y Alexandre Bouchard-Côté. "An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics". Systematic Biology 69, n.º 1 (6 de junio de 2019): 155–83. http://dx.doi.org/10.1093/sysbio/syz028.
Texto completoFinke, Axel, Arnaud Doucet y Adam M. Johansen. "Limit theorems for sequential MCMC methods". Advances in Applied Probability 52, n.º 2 (junio de 2020): 377–403. http://dx.doi.org/10.1017/apr.2020.9.
Texto completoCong-An, Xu, Xu Congqi, Dong Yunlong, Xiong Wei, Chai Yong y Li Tianmei. "A Novel Sequential Monte Carlo-Probability Hypothesis Density Filter for Particle Impoverishment Problem". Journal of Computational and Theoretical Nanoscience 13, n.º 10 (1 de octubre de 2016): 6872–77. http://dx.doi.org/10.1166/jctn.2016.5640.
Texto completoAbu Znaid, Ammar M. A., Mohd Yamani Idna Idris, Ainuddin Wahid Abdul Wahab, Liana Khamis Qabajeh y Omar Adil Mahdi. "Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review". Journal of Sensors 2017 (2017): 1–19. http://dx.doi.org/10.1155/2017/1430145.
Texto completoDeng, Yue, Yongzhen Pei, Changguo Li y Bin Zhu. "Model Selection and Parameter Estimation for an Improved Approximate Bayesian Computation Sequential Monte Carlo Algorithm". Discrete Dynamics in Nature and Society 2022 (30 de junio de 2022): 1–14. http://dx.doi.org/10.1155/2022/8969903.
Texto completoHsu, Kuo-Lin. "Hydrologic forecasting using artificial neural networks: a Bayesian sequential Monte Carlo approach". Journal of Hydroinformatics 13, n.º 1 (2 de abril de 2010): 25–35. http://dx.doi.org/10.2166/hydro.2010.044.
Texto completoWeng, Zhipeng, Jinghua Zhou y Zhengdong Zhan. "Reliability Evaluation of Standalone Microgrid Based on Sequential Monte Carlo Simulation Method". Energies 15, n.º 18 (14 de septiembre de 2022): 6706. http://dx.doi.org/10.3390/en15186706.
Texto completoRöder, Lenard L., Patrick Dewald, Clara M. Nussbaumer, Jan Schuladen, John N. Crowley, Jos Lelieveld y Horst Fischer. "Data quality enhancement for field experiments in atmospheric chemistry via sequential Monte Carlo filters". Atmospheric Measurement Techniques 16, n.º 5 (7 de marzo de 2023): 1167–78. http://dx.doi.org/10.5194/amt-16-1167-2023.
Texto completoNakano, S., K. Suzuki, K. Kawamura, F. Parrenin y T. Higuchi. "A sequential Bayesian approach for the estimation of the age–depth relationship of Dome Fuji ice core". Nonlinear Processes in Geophysics Discussions 2, n.º 3 (26 de junio de 2015): 939–68. http://dx.doi.org/10.5194/npgd-2-939-2015.
Texto completoRusyda Roslan, Nur Nabihah, NoorFatin Farhanie Mohd Fauzi y Mohd Ikhwan Muhammad Ridzuan. "Variance reduction technique in reliability evaluation for distribution system by using sequential Monte Carlo simulation". Bulletin of Electrical Engineering and Informatics 11, n.º 6 (1 de diciembre de 2022): 3061–68. http://dx.doi.org/10.11591/eei.v11i6.3950.
Texto completoTesis sobre el tema "Sequential Monte Carlo (SMC) method"
GONZATO, LUCA. "Application of Sequential Monte Carlo Methods to Dynamic Asset Pricing Models". Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/295144.
Texto completoIn this thesis we consider the application of Sequential Monte Carlo (SMC) methods to continuous-time asset pricing models. The first chapter of the thesis gives a self-contained overview on SMC methods. In particular, starting from basic Monte Carlo techniques we move to recent state of the art SMC algorithms. In the second chapter we review existing methods for the exact simulation of Hawkes processes. From our analysis we infer that the simulation scheme of Dassios and Zaho (2013) outperforms the other algorithms, including the most popular thinning method proposed by Ogata (1980). This chapter serves also as introduction to self-exciting jump processes, which are the subject of Chapter 3. Hence, in the third chapter we propose a new self-exciting jump diffusion model in order to describe oil price dynamics. We estimate the model by applying a state of the art SMC sampler on both spot and futures data. From the estimation results we find evidence of self-excitation in the oil market, which leads to an improved fit and a better out of sample futures forecasting performance with respect to jump-diffusion models with constant intensity. Furthermore, we compute and discuss two optimal hedging strategies based on futures trading. The optimality of the first hedging strategy proposed is based on the variance minimization, while the second strategy takes into account also the third-order moment contribution in considering the investors attitudes. A comparison between the two strategies in terms of hedging effectiveness is provided. Finally, in the fourth chapter we consider the estimation of continuous-time Wishart stochastic volatility models by observing portfolios of weighted options as in Orlowski (2019). In this framework we don't know the likelihood in closed-form; then we aim to estimate it using SMC techniques. To this end, we marginalize latent states and perform marginal likelihood estimation by adapting the recently proposed controlled SMC algorithm (Heng et. Al. 2019). From the numerical experiments we show that the proposed methodology gives much better results with respect to standard filtering techniques. Therefore, the great stability of our SMC method opens the door for effective joint estimation of latent states and unknown parameters in a Bayesian fashion. This last step amounts to design an SMC sampler based on a pseudo-marginal argument and is currently under preparation.
Ozgur, Soner. "Reduced Complexity Sequential Monte Carlo Algorithms for Blind Receivers". Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10518.
Texto completoCreal, 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.
Texto completoLang, Lixin. "Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science". The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1204582289.
Texto completoKuhlenschmidt, Bernd. "On the stability of sequential Monte Carlo methods for parameter estimation". Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709098.
Texto completoSkrivanek, Zachary. "Sequential Imputation and Linkage Analysis". The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1039121487.
Texto completoChen, Wen-shiang. "Bayesian estimation by sequential Monte Carlo sampling for nonlinear dynamic systems". Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1086146309.
Texto completoTitle from first page of PDF file. Document formatted into pages; contains xiv, 117 p. : ill. (some col.). Advisors: Bhavik R. Bakshi and Prem K. Goel, Department of Chemical Engineering. Includes bibliographical references (p. 114-117).
Valdes, LeRoy I. "Analysis Of Sequential Barycenter Random Probability Measures via Discrete Constructions". Thesis, University of North Texas, 2002. https://digital.library.unt.edu/ark:/67531/metadc3304/.
Texto completoFuglesang, Rutger. "Particle-Based Online Bayesian Learning of Static Parameters with Application to Mixture Models". Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279847.
Texto completoDetta examensarbete undersöker möjligheten att använda Sekventiella Monte Carlo metoder (SMC) för att utveckla en algoritm med syfte att utvinna parametrar i realtid givet en okänd modell. Då statistisk slutledning från dataströmmar medför svårigheter, särskilt i parameter-modeller, kommer arbetets fokus ligga i utvecklandet av en Monte Carlo algoritm vars uppgift är att sekvensiellt nyttja modellens posteriori fördelningar. Resultatet är att okända, statistiska parametrar kommer att förflyttas mot det krympande stödet av posterioren med hjälp utav en artificiell Markov dynamik, vilket tillåter en korrekt pseudo-marginalisering utav mål-distributionen. Algoritmen kommer sedan att testas på en enkel Gaussisk-modell, en Gaussisk mixturmodell (GMM) och till sist en GMM vars dimension är okänd. Kodningen i detta projekt har utförts i Matlab.
Carr, Michael John. "Estimating parameters of a stochastic cell invasion model with Fluorescent cell cycle labelling using approximate Bayesian computation". Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/226946/1/Michael_Carr_Thesis.pdf.
Texto completoLibros sobre el tema "Sequential Monte Carlo (SMC) method"
Arnaud, Doucet, De Freitas Nando y Gordon Neil 1967-, eds. Sequential Monte Carlo methods in practice. New York: Springer, 2001.
Buscar texto completoDoucet, Arnaud, Nando de Freitas, Neil Gordon y A. Smith. Sequential Monte Carlo Methods in Practice. Springer New York, 2010.
Buscar texto completo(Foreword), A. Smith, Arnaud Doucet (Editor), Nando de Freitas (Editor) y Neil Gordon (Editor), eds. Sequential Monte Carlo Methods in Practice (Statistics for Engineering and Information Science). Springer, 2001.
Buscar texto completoRubinstein, Reuven Y., Ad Ridder y Radislav Vaisman. Fast Sequential Monte Carlo Methods for Counting and Optimization. Wiley & Sons, Incorporated, John, 2013.
Buscar texto completoRubinstein, Reuven Y., Ad Ridder y Radislav Vaisman. Fast Sequential Monte Carlo Methods for Counting and Optimization. Wiley & Sons, Incorporated, John, 2013.
Buscar texto completoRubinstein, Reuven Y., Ad Ridder y Radislav Vaisman. Fast Sequential Monte Carlo Methods for Counting and Optimization. Wiley & Sons, Incorporated, John, 2013.
Buscar texto completoRubinstein, Reuven Y., Ad Ridder y Radislav Vaisman. Fast Sequential Monte Carlo Methods for Counting and Optimization. Wiley & Sons, Limited, John, 2013.
Buscar texto completoFast Sequential Monte Carlo Methods for Counting and Optimization Wiley Series in Probability and Statistics. John Wiley & Sons Inc, 2014.
Buscar texto completoBruno, Marcelo G. S. Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering. Morgan & Claypool Publishers, 2013.
Buscar texto completoBruno, Marcelo G. S. Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering. Morgan & Claypool Publishers, 2013.
Buscar texto completoCapítulos de libros sobre el tema "Sequential Monte Carlo (SMC) method"
Lundén, Daniel, Johannes Borgström y David Broman. "Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages". En Programming Languages and Systems, 404–31. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_15.
Texto completoLundén, Daniel, Joey Öhman, Jan Kudlicka, Viktor Senderov, Fredrik Ronquist y David Broman. "Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference". En Programming Languages and Systems, 29–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99336-8_2.
Texto completoMatsui, Atsushi, Simon Clippingdale y Takashi Matsumoto. "A Sequential Monte Carlo Method for Bayesian Face Recognition". En Lecture Notes in Computer Science, 578–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11815921_63.
Texto completoLundén, Daniel, Gizem Çaylak, Fredrik Ronquist y David Broman. "Automatic Alignment in Higher-Order Probabilistic Programming Languages". En Programming Languages and Systems, 535–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30044-8_20.
Texto completoPanayirci, E., H. A. Çirpan, M. Moeneclaey y N. Noels. "Blind Phase Noise Estimation in OFDM Systems by Sequential Monte Carlo Method". En Multi-Carrier Spread-Spectrum, 483–90. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-4437-2_52.
Texto completoSchikora, Marek, Wolfgang Koch, Roy Streit y Daniel Cremers. "A Sequential Monte Carlo Method for Multi-target Tracking with the Intensity Filter". En Advances in Intelligent Signal Processing and Data Mining, 55–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28696-4_3.
Texto completoVerly, G. "Sequential Gaussian Simulation: A Monte Carlo Method for Generating Models of Porosity and Permeability". En Generation, Accumulation and Production of Europe’s Hydrocarbons III, 345–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77859-9_28.
Texto completoJiang, Mingyan y Dongfeng Yuan. "Blind Estimation of Fast Time-Varying Multi-antenna Channels Based on Sequential Monte Carlo Method". En Lecture Notes in Computer Science, 482–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11538356_50.
Texto completoReich, Sebastian. "A Guided Sequential Monte Carlo Method for the Assimilation of Data into Stochastic Dynamical Systems". En Recent Trends in Dynamical Systems, 205–20. Basel: Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0451-6_10.
Texto completoWu, Yaohao, Wenying Liu y Chen Liang. "A Reliability Evaluation Method of Generation and Transmission Systems Based on Sequential Monte-Carlo Simulation". En Lecture Notes in Electrical Engineering, 467–74. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4981-2_51.
Texto completoActas de conferencias sobre el tema "Sequential Monte Carlo (SMC) method"
Colac¸o, Marcelo J., Helcio R. B. Orlande, Wellington B. da Silva y George S. Dulikravich. "Application of a Bayesian Filter to Estimate Unknown Heat Fluxes in a Natural Convection Problem". En ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47652.
Texto completoYousefian, Sajjad, Gilles Bourque, Sandeep Jella, Philippe Versailles y Rory F. D. Monaghan. "A Stochastic and Bayesian Inference Toolchain for Uncertainty and Risk Quantification of Rare Autoignition Events in DLE Premixers". En ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-83667.
Texto completoLee, Jae-young, Shahram Payandeh y Ljiljana Trajkovic´. "The Internet-Based Teleoperation: Motion and Force Predictions Using the Particle Filter Method". En ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-40495.
Texto completoGao, Hongzhi y Richard Green. "A sequential Monte Carlo method for particle filters". En 2008 23rd International Conference Image and Vision Computing New Zealand (IVCNZ). IEEE, 2008. http://dx.doi.org/10.1109/ivcnz.2008.4762108.
Texto completoWen, Quan y Jean Gao. "Tracking Interacting Subcellular Structures By Sequential Monte Carlo Method". En 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. http://dx.doi.org/10.1109/iembs.2007.4353259.
Texto completoMancasi, Monica y Ramona Vatu. "Smart grids reliability indices assessment using sequential Monte Carlo method". En 2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC). IEEE, 2015. http://dx.doi.org/10.1109/eeeic.2015.7165495.
Texto completoWen, Quan, Jean Gao y Kate Luby-Phelps. "Multiple Interacting Subcellular Structure Tracking by Sequential Monte Carlo Method". En 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007). IEEE, 2007. http://dx.doi.org/10.1109/bibm.2007.28.
Texto completoTan, Hui, Xinmeng Chen y Min Jiang. "Object Tracking based on Snake and Sequential Monte Carlo Method". En Sixth International Conference on Intelligent Systems Design and Applications. IEEE, 2006. http://dx.doi.org/10.1109/isda.2006.253863.
Texto completoHuda, A. S. N. y Rastko Zivanovic. "Distribution system reliability assessment using sequential multilevel Monte Carlo method". En 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia). IEEE, 2016. http://dx.doi.org/10.1109/isgt-asia.2016.7796499.
Texto completoErmolaev, Petr A., Maxim A. Volynsky y Pavel A. Skakov. "Evaluation of interference fringe parameters using sequential Monte Carlo method". En SPIE Optical Metrology, editado por Peter Lehmann, Wolfgang Osten y Armando Albertazzi Gonçalves. SPIE, 2015. http://dx.doi.org/10.1117/12.2184578.
Texto completoInformes sobre el tema "Sequential Monte Carlo (SMC) method"
Acton, Scott T. y Bing Li. A Sequential Monte Carlo Method for Real-time Tracking of Multiple Targets. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2010. http://dx.doi.org/10.21236/ada532576.
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