Littérature scientifique sur le sujet « MCMC optimization »
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Articles de revues sur le sujet "MCMC optimization"
Rong, Teng Zhong, et Zhi Xiao. « MCMC Sampling Statistical Method to Solve the Optimization ». Applied Mechanics and Materials 121-126 (octobre 2011) : 937–41. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.937.
Texte intégralZhang, Lihao, Zeyang Ye et Yuefan Deng. « Parallel MCMC methods for global optimization ». Monte Carlo Methods and Applications 25, no 3 (1 septembre 2019) : 227–37. http://dx.doi.org/10.1515/mcma-2019-2043.
Texte intégralMartino, L., V. Elvira, D. Luengo, J. Corander et F. Louzada. « Orthogonal parallel MCMC methods for sampling and optimization ». Digital Signal Processing 58 (novembre 2016) : 64–84. http://dx.doi.org/10.1016/j.dsp.2016.07.013.
Texte intégralYin, Long, Sheng Zhang, Kun Xiang, Yongqiang Ma, Yongzhen Ji, Ke Chen et Dongyu Zheng. « A New Stochastic Process of Prestack Inversion for Rock Property Estimation ». Applied Sciences 12, no 5 (25 février 2022) : 2392. http://dx.doi.org/10.3390/app12052392.
Texte intégralYang, Fan, et Jianwei Ren. « Reliability Analysis Based on Optimization Random Forest Model and MCMC ». Computer Modeling in Engineering & ; Sciences 125, no 2 (2020) : 801–14. http://dx.doi.org/10.32604/cmes.2020.08889.
Texte intégralGlynn, Peter W., Andrey Dolgin, Reuven Y. Rubinstein et Radislav Vaisman. « HOW TO GENERATE UNIFORM SAMPLES ON DISCRETE SETS USING THE SPLITTING METHOD ». Probability in the Engineering and Informational Sciences 24, no 3 (23 avril 2010) : 405–22. http://dx.doi.org/10.1017/s0269964810000057.
Texte intégralLi, Chunyuan, Changyou Chen, Yunchen Pu, Ricardo Henao et Lawrence Carin. « Communication-Efficient Stochastic Gradient MCMC for Neural Networks ». Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 juillet 2019) : 4173–80. http://dx.doi.org/10.1609/aaai.v33i01.33014173.
Texte intégralYamaguchi, Kazuhiro, et Kensuke Okada. « Variational Bayes Inference for the DINA Model ». Journal of Educational and Behavioral Statistics 45, no 5 (31 mars 2020) : 569–97. http://dx.doi.org/10.3102/1076998620911934.
Texte intégralXu, Haoyu, Tao Zhang, Yiqi Luo, Xin Huang et Wei Xue. « Parameter calibration in global soil carbon models using surrogate-based optimization ». Geoscientific Model Development 11, no 7 (27 juillet 2018) : 3027–44. http://dx.doi.org/10.5194/gmd-11-3027-2018.
Texte intégralKitchen, James L., Jonathan D. Moore, Sarah A. Palmer et Robin G. Allaby. « MCMC-ODPR : Primer design optimization using Markov Chain Monte Carlo sampling ». BMC Bioinformatics 13, no 1 (2012) : 287. http://dx.doi.org/10.1186/1471-2105-13-287.
Texte intégralThèses sur le sujet "MCMC optimization"
Mahendran, Nimalan. « Bayesian optimization for adaptive MCMC ». Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/30636.
Texte intégralKarimi, Belhal. « Non-Convex Optimization for Latent Data Models : Algorithms, Analysis and Applications ». Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLX040/document.
Texte intégralMany problems in machine learning pertain to tackling the minimization of a possibly non-convex and non-smooth function defined on a Many problems in machine learning pertain to tackling the minimization of a possibly non-convex and non-smooth function defined on a Euclidean space.Examples include topic models, neural networks or sparse logistic regression.Optimization methods, used to solve those problems, have been widely studied in the literature for convex objective functions and are extensively used in practice.However, recent breakthroughs in statistical modeling, such as deep learning, coupled with an explosion of data samples, require improvements of non-convex optimization procedure for large datasets.This thesis is an attempt to address those two challenges by developing algorithms with cheaper updates, ideally independent of the number of samples, and improving the theoretical understanding of non-convex optimization that remains rather limited.In this manuscript, we are interested in the minimization of such objective functions for latent data models, ie, when the data is partially observed which includes the conventional sense of missing data but is much broader than that.In the first part, we consider the minimization of a (possibly) non-convex and non-smooth objective function using incremental and online updates.To that end, we propose several algorithms exploiting the latent structure to efficiently optimize the objective and illustrate our findings with numerous applications.In the second part, we focus on the maximization of non-convex likelihood using the EM algorithm and its stochastic variants.We analyze several faster and cheaper algorithms and propose two new variants aiming at speeding the convergence of the estimated parameters
Chaari, Lotfi. « Parallel magnetic resonance imaging reconstruction problems using wavelet representations ». Phd thesis, Université Paris-Est, 2010. http://tel.archives-ouvertes.fr/tel-00587410.
Texte intégralPark, Jee Hyuk. « On the separation of preferences among marked point process wager alternatives ». [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2757.
Texte intégralBardenet, Rémi. « Towards adaptive learning and inference : applications to hyperparameter tuning and astroparticle physics ». Thesis, Paris 11, 2012. http://www.theses.fr/2012PA112307.
Texte intégralInference and optimization algorithms usually have hyperparameters that require to be tuned in order to achieve efficiency. We consider here different approaches to efficiently automatize the hyperparameter tuning step by learning online the structure of the addressed problem. The first half of this thesis is devoted to hyperparameter tuning in machine learning. After presenting and improving the generic sequential model-based optimization (SMBO) framework, we show that SMBO successfully applies to the task of tuning the numerous hyperparameters of deep belief networks. We then propose an algorithm that performs tuning across datasets, mimicking the memory that humans have of past experiments with the same algorithm on different datasets. The second half of this thesis deals with adaptive Markov chain Monte Carlo (MCMC) algorithms, sampling-based algorithms that explore complex probability distributions while self-tuning their internal parameters on the fly. We start by describing the Pierre Auger observatory, a large-scale particle physics experiment dedicated to the observation of atmospheric showers triggered by cosmic rays. The models involved in the analysis of Auger data motivated our study of adaptive MCMC. We derive the first part of the Auger generative model and introduce a procedure to perform inference on shower parameters that requires only this bottom part. Our model inherently suffers from label switching, a common difficulty in MCMC inference, which makes marginal inference useless because of redundant modes of the target distribution. After reviewing existing solutions to label switching, we propose AMOR, the first adaptive MCMC algorithm with online relabeling. We give both an empirical and theoretical study of AMOR, unveiling interesting links between relabeling algorithms and vector quantization
Cheng, Yougan. « Computational Models of Brain Energy Metabolism at Different Scales ». Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1396534897.
Texte intégralThouvenin, Pierre-Antoine. « Modeling spatial and temporal variabilities in hyperspectral image unmixing ». Phd thesis, Toulouse, INPT, 2017. http://oatao.univ-toulouse.fr/19258/1/THOUVENIN_PierreAntoine.pdf.
Texte intégralDiabaté, Modibo. « Modélisation stochastique et estimation de la croissance tumorale ». Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM040.
Texte intégralThis thesis is about mathematical modeling of cancer dynamics ; it is divided into two research projects.In the first project, we estimate the parameters of the deterministic limit of a stochastic process modeling the dynamics of melanoma (skin cancer) treated by immunotherapy. The estimation is carried out with a nonlinear mixed-effect statistical model and the SAEM algorithm, using real data of tumor size. With this mathematical model that fits the data well, we evaluate the relapse probability of melanoma (using the Importance Splitting algorithm), and we optimize the treatment protocol (doses and injection times).We propose in the second project, a likelihood approximation method based on an approximation of the Belief Propagation algorithm by the Expectation-Propagation algorithm, for a diffusion approximation of the melanoma stochastic model, noisily observed in a single individual. This diffusion approximation (defined by a stochastic differential equation) having no analytical solution, we approximate its solution by using an Euler method (after testing the Euler method on the Ornstein Uhlenbeck diffusion process). Moreover, a moment approximation method is used to manage the multidimensionality and the non-linearity of the melanoma mathematical model. With the likelihood approximation method, we tackle the problem of parameter estimation in Hidden Markov Models
Kim, Tae Seon. « Modeling, optimization, and control of via formation by photosensitive polymers for MCM-D applications ». Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/15017.
Texte intégralAl-Hasani, Firas Ali Jawad. « Multiple Constant Multiplication Optimization Using Common Subexpression Elimination and Redundant Numbers ». Thesis, University of Canterbury. Electrical and Computer Engineering, 2014. http://hdl.handle.net/10092/9054.
Texte intégralChapitres de livres sur le sujet "MCMC optimization"
Bhatnagar, Nayantara, Andrej Bogdanov et Elchanan Mossel. « The Computational Complexity of Estimating MCMC Convergence Time ». Dans Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 424–35. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22935-0_36.
Texte intégralSzirányi, Tamás, et Zoltán Tóth. « Optimization of Paintbrush Rendering of Images by Dynamic MCMC Methods ». Dans Lecture Notes in Computer Science, 201–15. Berlin, Heidelberg : Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44745-8_14.
Texte intégralFox, Colin. « Polynomial Accelerated MCMC and Other Sampling Algorithms Inspired by Computational Optimization ». Dans Monte Carlo and Quasi-Monte Carlo Methods 2012, 349–66. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41095-6_15.
Texte intégralSommer, Katrin, et Claus Weihs. « Using MCMC as a Stochastic Optimization Procedure for Monophonic and Polyphonic Sound ». Dans Studies in Classification, Data Analysis, and Knowledge Organization, 645–52. Berlin, Heidelberg : Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-70981-7_74.
Texte intégralNijkamp, Erik, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu et Ying Nian Wu. « Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference ». Dans Computer Vision – ECCV 2020, 361–78. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58539-6_22.
Texte intégralPeeters, Joris, et Eric Beyne. « Analysis and optimization of circuit interconnect performance ». Dans MCM C/Mixed Technologies and Thick Film Sensors, 29–34. Dordrecht : Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0079-3_4.
Texte intégralKumm, Martin. « Optimally Solving MCM Related Problems Using Integer Linear Programming ». Dans Multiple Constant Multiplication Optimizations for Field Programmable Gate Arrays, 87–111. Wiesbaden : Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13323-8_5.
Texte intégralSaini, Suman, Jyoti Chawla, Rajeev Kumar et Inderpreet Kaur. « Optimization of Lead Ions Adsorption onto C16-6-16 Incorporated Mesoporous MCM-41 Using Box-Behnken Design ». Dans Environmental Biotechnology For Soil and Wastewater Implications on Ecosystems, 61–67. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6846-2_9.
Texte intégralDiekmann, Odo, Hans Heesterbeek et Tom Britton. « A brief guide to computer intensive statistics ». Dans Mathematical Tools for Understanding Infectious Disease Dynamics. Princeton University Press, 2012. http://dx.doi.org/10.23943/princeton/9780691155395.003.0015.
Texte intégralDiekmann, Odo, Hans Heesterbeek et Tom Britton. « Elaborations for Part I ». Dans Mathematical Tools for Understanding Infectious Disease Dynamics. Princeton University Press, 2012. http://dx.doi.org/10.23943/princeton/9780691155395.003.0016.
Texte intégralActes de conférences sur le sujet "MCMC optimization"
Acar, Erdem, et Gamze Bayrak. « Reliability Estimation Using MCMC Based Tail Modeling ». Dans 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virginia : American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-4412.
Texte intégralCannella, Chris, et Vahid Tarokh. « Semi-Empirical Objective Functions for MCMC Proposal Optimization ». Dans 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956603.
Texte intégralZhao, Zinan, et Mrinal Kumar. « A Split-Bernstein/MCMC Approach to Probabilistically Constrained Optimization ». Dans AIAA Guidance, Navigation, and Control Conference. Reston, Virginia : American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-1084.
Texte intégralXiang, K., et L. Han. « Adaptive Particle Swarm Optimization Assisted MCMC for Stochastic Inversion ». Dans 81st EAGE Conference and Exhibition 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201901305.
Texte intégralShadbakht, Sormeh, et Babak Hassibi. « MCMC methods for entropy optimization and nonlinear network coding ». Dans 2010 IEEE International Symposium on Information Theory - ISIT. IEEE, 2010. http://dx.doi.org/10.1109/isit.2010.5513737.
Texte intégralWu, Y.-T. (Justin). « MCMC-Based Simulation Method for Efficient Risk- Based Maintenance Optimization ». Dans SAE World Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States : SAE International, 2009. http://dx.doi.org/10.4271/2009-01-0566.
Texte intégralKim, Wonsik, et Kyoung Mu Lee. « Scanline Sampler without Detailed Balance : An Efficient MCMC for MRF Optimization ». Dans 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.176.
Texte intégralLi Tang, Zheng Zhao, Xiu-Jun Gong et Hua-Peng Zeng. « Optimization of MCMC sampling algorithm for the calculation of PAC-Bayes bound ». Dans 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890745.
Texte intégralSantoso, Ryan, Xupeng He, Marwa Alsinan, Hyung Kwak et Hussein Hoteit. « Bayesian Long-Short Term Memory for History Matching in Reservoir Simulations ». Dans SPE Reservoir Simulation Conference. SPE, 2021. http://dx.doi.org/10.2118/203976-ms.
Texte intégralMatsumoto, Nobuyuki, Masafumi Fukuma et Naoya Umeda. « Distance between configurations in MCMC simulations and the geometrical optimization of the tempering algorithms ». Dans 37th International Symposium on Lattice Field Theory. Trieste, Italy : Sissa Medialab, 2020. http://dx.doi.org/10.22323/1.363.0168.
Texte intégralRapports d'organisations sur le sujet "MCMC optimization"
Franzon, Paul D. Methodology, Tools and Demonstration of MCM System Optimization. Fort Belvoir, VA : Defense Technical Information Center, juillet 1997. http://dx.doi.org/10.21236/ada328732.
Texte intégralBrown, Richard B. Design Optimization of a GaAs RISC Microprocessor with Area-Interconnect MCM Packaging. Fort Belvoir, VA : Defense Technical Information Center, décembre 1999. http://dx.doi.org/10.21236/ada379011.
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