Relevant bibliographies by topics / Algorithme MCMC hybride

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Algorithme MCMC hybride'

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

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Journal articles on the topic "Algorithme MCMC hybride"

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Liu, Shun Lan, and Lin Wang. "New Hybrid Blind Equalization Algorithms." Applied Mechanics and Materials 182-183 (June 2012): 1810–15. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1810.

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A novel decision-directed Modified Constant Modulus Algorithm (DD-MCMA) was proposed firstly. Then a constellation matched error (CME) function was added to the cost function of DD-MCMA and CME-DD-MCMA algorithm was presented. Furthermore, we improve the CME-DD-MCMA by replacing the fixed step with variable step size, that is VSS-CME-DD-MCMA algorithm. The simulation results show that the proposed new blind equalization algorithms can tremendously accelerate the convergence speed and achieve lower residual inter-symbol interference (ISI) than MCMA, and among the three proposed algorithms, VSS-
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Elkhechafi, Mariam, Hanaa Hachimi, and Youssfi Elkettani. "A new hybrid cuckoo search and firefly optimization." Monte Carlo Methods and Applications 24, no. 1 (2018): 71–77. http://dx.doi.org/10.1515/mcma-2018-0003.

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Abstract In this paper, we present a new hybrid algorithm which is a combination of a hybrid Cuckoo search algorithm and Firefly optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, Cuckoo Search (CS) and Firefly Algorithm (FA) for the global optimization. Denoted as CS-FA. The hybrid CS-FA technique incorporates concepts from CS and FA and creates individuals in a new generation not only by random walk as found in CS but also by mechanisms of FA. To analyze the benefits of hybridization, we have comparatively evaluated the classical Cucko
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Pinski, Francis J. "A Novel Hybrid Monte Carlo Algorithm for Sampling Path Space." Entropy 23, no. 5 (2021): 499. http://dx.doi.org/10.3390/e23050499.

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To sample from complex, high-dimensional distributions, one may choose algorithms based on the Hybrid Monte Carlo (HMC) method. HMC-based algorithms generate nonlocal moves alleviating diffusive behavior. Here, I build on an already defined HMC framework, hybrid Monte Carlo on Hilbert spaces (Beskos, et al. Stoch. Proc. Applic. 2011), that provides finite-dimensional approximations of measures π, which have density with respect to a Gaussian measure on an infinite-dimensional Hilbert (path) space. In all HMC algorithms, one has some freedom to choose the mass operator. The novel feature of the
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Zhou, Qingping, Zixi Hu, Zhewei Yao, and Jinglai Li. "A Hybrid Adaptive MCMC Algorithm in Function Spaces." SIAM/ASA Journal on Uncertainty Quantification 5, no. 1 (2017): 621–39. http://dx.doi.org/10.1137/16m1082950.

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Ahmadian, Yashar, Jonathan W. Pillow, and Liam Paninski. "Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains." Neural Computation 23, no. 1 (2011): 46–96. http://dx.doi.org/10.1162/neco_a_00059.

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Stimulus reconstruction or decoding methods provide an important tool for understanding how sensory and motor information is represented in neural activity. We discuss Bayesian decoding methods based on an encoding generalized linear model (GLM) that accurately describes how stimuli are transformed into the spike trains of a group of neurons. The form of the GLM likelihood ensures that the posterior distribution over the stimuli that caused an observed set of spike trains is log concave so long as the prior is. This allows the maximum a posteriori (MAP) stimulus estimate to be obtained using e
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Cheng, Hsien-Chie, I.-Chun Chung, and Wen-Hwa Chen. "Thermal Chip Placement in MCMs Using a Novel Hybrid Optimization Algorithm." IEEE Transactions on Components, Packaging and Manufacturing Technology 2, no. 5 (2012): 764–74. http://dx.doi.org/10.1109/tcpmt.2012.2188396.

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Jiang, Yu, Yoko Hoshi, Manabu Machida, and Gen Nakamura. "A Hybrid Inversion Scheme Combining Markov Chain Monte Carlo and Iterative Methods for Determining Optical Properties of Random Media." Applied Sciences 9, no. 17 (2019): 3500. http://dx.doi.org/10.3390/app9173500.

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Near-infrared spectroscopy (NIRS) including diffuse optical tomography is an imaging modality which makes use of diffuse light propagation in random media. When optical properties of a random medium are investigated from boundary measurements of reflected or transmitted light, iterative inversion schemes such as the Levenberg–Marquardt algorithm are known to fail when initial guesses are not close enough to the true value of the coefficient to be reconstructed. In this paper, we investigate how this weakness of iterative schemes is overcome using Markov chain Monte Carlo. Using time-resolved m
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Sarasvathi, V., N. Ch S. N. Iyengar, and Snehanshu Saha. "QoS Guaranteed Intelligent Routing Using Hybrid PSO-GA in Wireless Mesh Networks." Cybernetics and Information Technologies 15, no. 1 (2015): 69–83. http://dx.doi.org/10.1515/cait-2015-0007.

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Abstract In Multi-Channel Multi-Radio Wireless Mesh Networks (MCMR-WMN), finding the optimal routing by satisfying the Quality of Service (QoS) constraints is an ambitious task. Multiple paths are available from the source node to the gateway for reliability, and sometimes it is necessary to deal with failures of the link in WMN. A major challenge in a MCMR-WMN is finding the routing with QoS satisfied and an interference free path from the redundant paths, in order to transmit the packets through this path. The Particle Swarm Optimization (PSO) is an optimization technique to find the candida
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Tiwari, R. K., and S. Maiti. "Bayesian neural network modeling of tree-ring temperature variability record from the Western Himalayas." Nonlinear Processes in Geophysics 18, no. 4 (2011): 515–28. http://dx.doi.org/10.5194/npg-18-515-2011.

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Abstract. A novel technique based on the Bayesian neural network (BNN) theory is developed and employed to model the temperature variation record from the Western Himalayas. In order to estimate an a posteriori probability function, the BNN is trained with the Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) simulations algorithm. The efficacy of the new algorithm is tested on the well known chaotic, first order autoregressive (AR) and random models and then applied to model the temperature variation record decoded from the tree-ring widths of the Western Himalayas for the period spann
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Maiti, S., G. Gupta, V. C. Erram, and R. K. Tiwari. "Inversion of Schlumberger resistivity sounding data from the critically dynamic Koyna region using the Hybrid Monte Carlo-based neural network approach." Nonlinear Processes in Geophysics 18, no. 2 (2011): 179–92. http://dx.doi.org/10.5194/npg-18-179-2011.

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Abstract. Koyna region is well-known for its triggered seismic activities since the hazardous earthquake of M=6.3 occurred around the Koyna reservoir on 10 December 1967. Understanding the shallow distribution of resistivity pattern in such a seismically critical area is vital for mapping faults, fractures and lineaments. However, deducing true resistivity distribution from the apparent resistivity data lacks precise information due to intrinsic non-linearity in the data structures. Here we present a new technique based on the Bayesian neural network (BNN) theory using the concept of Hybrid Mo
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Dissertations / Theses on the topic "Algorithme MCMC hybride"

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Fu, Shuai. "Inversion probabiliste bayésienne en analyse d'incertitude." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00766341.

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Ce travail de recherche propose une solution aux problèmes inverses probabilistes avec des outils de la statistique bayésienne. Le problème inverse considéré est d'estimer la distribution d'une variable aléatoire non observée X a partir d'observations bruitées Y suivant un modèle physique coûteux H. En général, de tels problèmes inverses sont rencontrés dans le traitement des incertitudes. Le cadre bayésien nous permet de prendre en compte les connaissances préalables d'experts surtout avec peu de données disponibles. Un algorithme de Metropolis-Hastings-within-Gibbs est proposé pour approcher
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Fu, Shuai. "Inverse problems occurring in uncertainty analysis." Thesis, Paris 11, 2012. http://www.theses.fr/2012PA112208/document.

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Ce travail de recherche propose une solution aux problèmes inverses probabilistes avec des outils de la statistique bayésienne. Le problème inverse considéré est d'estimer la distribution d'une variable aléatoire non observée X à partir d'observations bruitées Y suivant un modèle physique coûteux H. En général, de tels problèmes inverses sont rencontrés dans le traitement des incertitudes. Le cadre bayésien nous permet de prendre en compte les connaissances préalables d'experts en particulier lorsque peu de données sont disponibles. Un algorithme de Metropolis-Hastings-within-Gibbs est proposé
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Book chapters on the topic "Algorithme MCMC hybride"

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Chaudhury, Bhaskar, Mihir Shah, Unnati Parekh, et al. "Hybrid Parallelization of Particle in Cell Monte Carlo Collision (PIC-MCC) Algorithm for Simulation of Low Temperature Plasmas." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7729-7_3.

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Conference papers on the topic "Algorithme MCMC hybride"

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Wang, Yongbo, Huapeng Wu, and Heikki Handroos. "Identifiable Parameter Analysis for the Kinematic Calibration of a Hybrid Robot." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47573.

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In this paper, a statistical method for the determination of the identifiable parameters of a hybrid serial-parallel robot IWR (Intersector Welding Robot) is presented. This method is based on the Markov Chain Monte Carlo (MCMC) algorithm to analyze the posterior distribution and correlation of the error parameters. Differential Evolution algorithm is employed to search a global optimizer as initial values for the random sampling of MCMC. The robot under study has ten degrees of freedom (DOF) and will be used to carry out welding, machining, and remote handing for the assembly of vacuum vessel
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Wang, Danling, John Morris, Qin Zhang, and Quanfeng Gu. "Hybrid parallel sequential Monte Carlo algorithm combining MCMC and auxiliary variable." In Second International Conference on Digital Image Processing. SPIE, 2010. http://dx.doi.org/10.1117/12.855670.

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Ocaña, Kary, Micaella Coelho, Guilherme Freire, and Carla Osthoff. "High-Performance Computing of BEAST/BEAGLE in Bayesian Phylogenetics using SDumont Hybrid Resources." In Brazilian e-Science Workshop. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/bresci.2020.11190.

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Bayesian phylogenetic algorithms are computationally intensive. BEAST 1.10 inferences made use of the BEAGLE 3 high-performance library for efficient likelihood computations. The strategy allows phylogenetic inference and dating in current knowledge for SARS-CoV-2 transmission. Follow-up simulations on hybrid resources of Santos Dumont supercomputer using four phylogenomic data sets, we characterize the scaling performance behavior of BEAST 1.10. Our results provide insight into the species tree and MCMC chain length estimation, identifying preferable requirements to improve the use of high-pe
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Zwirglmaier, Kilian, and Daniel Straub. "Hybrid Bayesian Network Algorithm based on MCMC and Subset Simulation for Reliability Analysis." In Proceedings of the 29th European Safety and Reliability Conference (ESREL). Research Publishing Services, 2019. http://dx.doi.org/10.3850/978-981-11-2724-3_0828-cd.

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Liu, Bin, Jie Zhang, Kai-Kit Wong, Liwen He, and Chengpeng Hao. "Low-latency near-capacity MIMO detection using parallel and hybrid QRD-MCMC algorithm." In 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP). IEEE, 2015. http://dx.doi.org/10.1109/chinasip.2015.7230535.

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Niaki, Farbod Akhavan, Durul Ulutan, and Laine Mears. "Parameter Estimation Using Markov Chain Monte Carlo Method in Mechanistic Modeling of Tool Wear During Milling." In ASME 2015 International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/msec2015-9357.

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Several models have been proposed to describe the relationship between cutting parameters and machining outputs such as cutting forces and tool wear. However, these models usually cannot be generalized, due to the inherent uncertainties that exist in the process. These uncertainties may originate from machining, workpiece material composition, and measurements. A stochastic approach can be utilized to compensate for the lack of certainty in machining, particularly for tool wear evolution. The Markov Chain Monte Carlo (MCMC) method is a powerful tool for addressing uncertainties in machining pa
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Lei, Chen. "A Hybrid Optimization Approach of Max-Min Ant System and Adaptive Genetic Algorithm for MCM Interconnect Test Generation." In 2007 8th International Conference on Electronic Packaging Technology. IEEE, 2007. http://dx.doi.org/10.1109/icept.2007.4441391.

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