Artigos de revistas sobre o tema "Robbins-Monro algorithm"
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Moler, José A., Fernando Plo e Miguel San Miguel. "Adaptive designs and Robbins–Monro algorithm". Journal of Statistical Planning and Inference 131, n.º 1 (abril de 2005): 161–74. http://dx.doi.org/10.1016/j.jspi.2003.12.018.
Texto completo da fonteWardi, Y. "On a proof of a Robbins-Monro algorithm". Journal of Optimization Theory and Applications 64, n.º 1 (janeiro de 1990): 217. http://dx.doi.org/10.1007/bf00940033.
Texto completo da fonteLin, Siming, e Jennie Si. "Weight-Value Convergence of the SOM Algorithm for Discrete Input". Neural Computation 10, n.º 4 (1 de maio de 1998): 807–14. http://dx.doi.org/10.1162/089976698300017485.
Texto completo da fonteMoser, Barry Kurt, e Melinda H. McCann. "Algorithm AS 316: A Robbins-Monro-based Sequential Procedure". Journal of the Royal Statistical Society: Series C (Applied Statistics) 46, n.º 3 (1997): 388–99. http://dx.doi.org/10.1111/1467-9876.00078.
Texto completo da fonteEl Moumen, AbdelKader, Salim Benslimane e Samir Rahmani. "Robbins–Monro Algorithm with $$\boldsymbol{\psi}$$-Mixing Random Errors". Mathematical Methods of Statistics 31, n.º 3 (setembro de 2022): 105–19. http://dx.doi.org/10.3103/s1066530722030024.
Texto completo da fonteXU, ZI, YINGYING LI e XINGFANG ZHAO. "SIMULATION-BASED OPTIMIZATION BY NEW STOCHASTIC APPROXIMATION ALGORITHM". Asia-Pacific Journal of Operational Research 31, n.º 04 (agosto de 2014): 1450026. http://dx.doi.org/10.1142/s0217595914500262.
Texto completo da fonteCai, Li. "Metropolis-Hastings Robbins-Monro Algorithm for Confirmatory Item Factor Analysis". Journal of Educational and Behavioral Statistics 35, n.º 3 (junho de 2010): 307–35. http://dx.doi.org/10.3102/1076998609353115.
Texto completo da fonteChen, Han-Fu. "Stochastic approximation with non-additive measurement noise". Journal of Applied Probability 35, n.º 2 (junho de 1998): 407–17. http://dx.doi.org/10.1239/jap/1032192856.
Texto completo da fonteChen, Han-Fu. "Stochastic approximation with non-additive measurement noise". Journal of Applied Probability 35, n.º 02 (junho de 1998): 407–17. http://dx.doi.org/10.1017/s0021900200015035.
Texto completo da fonteBuckland, S. T., e P. H. Garthwaite. "Algorithm AS 259: Estimating Confidence Intervals by the Robbins-Monro Search Process". Applied Statistics 39, n.º 3 (1990): 413. http://dx.doi.org/10.2307/2347401.
Texto completo da fonteQi, Anna, Lihua Yang e Chao Huang. "Convergence of Markovian stochastic approximation for Markov random fields with hidden variables". Stochastics and Dynamics 20, n.º 05 (18 de novembro de 2019): 2050029. http://dx.doi.org/10.1142/s021949372050029x.
Texto completo da fonteBarczy, Mátyás, Ábris Nagy, Csaba Noszály e Csaba Vincze. "A Robbins–Monro-type algorithm for computing global minimizer of generalized conic functions". Optimization 64, n.º 9 (19 de maio de 2014): 1999–2020. http://dx.doi.org/10.1080/02331934.2014.919499.
Texto completo da fonteChen, Han-Fu, Lei Guo e Ai-Jun Gao. "Convergence and robustness of the Robbins-Monro algorithm truncated at randomly varying bounds". Stochastic Processes and their Applications 27 (1987): 217–31. http://dx.doi.org/10.1016/0304-4149(87)90039-1.
Texto completo da fonteCai, Li. "High-dimensional Exploratory Item Factor Analysis by A Metropolis–Hastings Robbins–Monro Algorithm". Psychometrika 75, n.º 1 (28 de julho de 2009): 33–57. http://dx.doi.org/10.1007/s11336-009-9136-x.
Texto completo da fonteGahbiche, Mouna, e Mariane Pelletier. "On the estimation of the asymptotic covariance matrix for the averaged Robbins–Monro algorithm". Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 331, n.º 3 (agosto de 2000): 255–60. http://dx.doi.org/10.1016/s0764-4442(00)01595-0.
Texto completo da fonteLiu, Chen-Wei, Björn Andersson e Anders Skrondal. "A Constrained Metropolis–Hastings Robbins–Monro Algorithm for Q Matrix Estimation in DINA Models". Psychometrika 85, n.º 2 (junho de 2020): 322–57. http://dx.doi.org/10.1007/s11336-020-09707-4.
Texto completo da fonteKrasulina, T. P., e Yu O. Yatel. "A probability estimate for not exceeding the unknown threshold by the Robbins-Monro algorithm". Automation and Remote Control 66, n.º 3 (março de 2005): 422–26. http://dx.doi.org/10.1007/s10513-005-0071-8.
Texto completo da fonteZhao, Wenxiao. "An Introduction to Development of Centralized and Distributed Stochastic Approximation Algorithm with Expanding Truncations". Algorithms 14, n.º 6 (31 de maio de 2021): 174. http://dx.doi.org/10.3390/a14060174.
Texto completo da fonteHeidergott, Bernd. "A weak derivative approach to optimization of threshold parameters in a multicomponent maintenance system". Journal of Applied Probability 38, n.º 2 (junho de 2001): 386–406. http://dx.doi.org/10.1239/jap/996986751.
Texto completo da fonteHeidergott, Bernd. "A weak derivative approach to optimization of threshold parameters in a multicomponent maintenance system". Journal of Applied Probability 38, n.º 02 (junho de 2001): 386–406. http://dx.doi.org/10.1017/s0021900200019926.
Texto completo da fonteFermin, Lisandro Javier, Ricardo Rios e Luis Angel Rodriguez. "A Robbins-Monro Algorithm for Non-Parametric Estimation of NAR Process with Markov Switching: Consistency". Journal of Time Series Analysis 38, n.º 6 (20 de abril de 2017): 809–37. http://dx.doi.org/10.1111/jtsa.12237.
Texto completo da fonteGodichon-Baggioni, Antoine, e Bruno Portier. "An averaged projected Robbins-Monro algorithm for estimating the parameters of a truncated spherical distribution". Electronic Journal of Statistics 11, n.º 1 (2017): 1890–927. http://dx.doi.org/10.1214/17-ejs1276.
Texto completo da fonteMonroe, Scott, e Li Cai. "Estimation of a Ramsay-Curve Item Response Theory Model by the Metropolis–Hastings Robbins–Monro Algorithm". Educational and Psychological Measurement 74, n.º 2 (3 de setembro de 2013): 343–69. http://dx.doi.org/10.1177/0013164413499344.
Texto completo da fonteMontes, Francisco, e Jorge Mateu. "On the MLE for a spatial point pattern". Advances in Applied Probability 28, n.º 2 (junho de 1996): 339. http://dx.doi.org/10.1017/s0001867800048382.
Texto completo da fonteSlaoui, Y., e A. Jmaei. "Recursive and non-recursive regression estimators using Bernstein polynomials". Theory of Stochastic Processes 26(42), n.º 1 (27 de dezembro de 2022): 60–95. http://dx.doi.org/10.37863/tsp-2899660400-77.
Texto completo da fonteYang, Ji Seung, e Li Cai. "Estimation of Contextual Effects Through Nonlinear Multilevel Latent Variable Modeling With a Metropolis–Hastings Robbins–Monro Algorithm". Journal of Educational and Behavioral Statistics 39, n.º 6 (dezembro de 2014): 550–82. http://dx.doi.org/10.3102/1076998614559972.
Texto completo da fonteBashkov, Bozhidar M., e Christine E. DeMars. "Examining the Performance of the Metropolis–Hastings Robbins–Monro Algorithm in the Estimation of Multilevel Multidimensional IRT Models". Applied Psychological Measurement 41, n.º 5 (1 de fevereiro de 2017): 323–37. http://dx.doi.org/10.1177/0146621616688923.
Texto completo da fonteFindley, David F. "Convergence of a Robbins-Monro Algorithm for Recursive Estimation with Non-Monotone Weights for a Function with a Restricted Domain and Multiple Zeros". Calcutta Statistical Association Bulletin 56, n.º 1-4 (março de 2005): 1–15. http://dx.doi.org/10.1177/0008068320050501.
Texto completo da fonteLi, Menglin, Xueqiang Gu, Chengyi Zeng e Yuan Feng. "Feasibility Analysis and Application of Reinforcement Learning Algorithm Based on Dynamic Parameter Adjustment". Algorithms 13, n.º 9 (22 de setembro de 2020): 239. http://dx.doi.org/10.3390/a13090239.
Texto completo da fonteArouna, Bouhari. "Robbins–Monro algorithms and variance reduction in finance". Journal of Computational Finance 7, n.º 2 (2003): 35–61. http://dx.doi.org/10.21314/jcf.2003.111.
Texto completo da fonteGarthwaite, P. H., Y. Fan e S. A. Sisson. "Adaptive optimal scaling of Metropolis–Hastings algorithms using the Robbins–Monro process". Communications in Statistics - Theory and Methods 45, n.º 17 (5 de julho de 2016): 5098–111. http://dx.doi.org/10.1080/03610926.2014.936562.
Texto completo da fonteQian-Yu Tang, Han-Fu Chen e Zeng-Jin Han. "Convergence rates of perturbation-analysis-Robbins-Monro-single-run algorithms for single server queues". IEEE Transactions on Automatic Control 42, n.º 10 (1997): 1442–47. http://dx.doi.org/10.1109/9.633835.
Texto completo da fonteDereich, Steffen, e Thomas Müller-Gronbach. "General multilevel adaptations for stochastic approximation algorithms of Robbins–Monro and Polyak–Ruppert type". Numerische Mathematik 142, n.º 2 (6 de fevereiro de 2019): 279–328. http://dx.doi.org/10.1007/s00211-019-01024-y.
Texto completo da fonteLambrou, George I., Kyriaki Hatziagapiou, Petros Toumpaniaris, Penelope Ioannidou e Dimitrios Koutsouris. "Computational Modelling in Epidemiological Dispersion Using Diffusion and Epidemiological Equations". International Journal of Reliable and Quality E-Healthcare 8, n.º 4 (outubro de 2019): 1–37. http://dx.doi.org/10.4018/ijrqeh.2019100101.
Texto completo da fonteLiu, Chen-Wei. "Efficient Metropolis-Hastings Robbins-Monro Algorithm for High-Dimensional Diagnostic Classification Models". Applied Psychological Measurement, 8 de setembro de 2022, 014662162211239. http://dx.doi.org/10.1177/01466216221123981.
Texto completo da fonteLin, Xiaofan, Siliang Zhang, Yincai Tang e Xuan Li. "A Gibbs‐INLA algorithm for multidimensional graded response model analysis". British Journal of Mathematical and Statistical Psychology, 29 de setembro de 2023. http://dx.doi.org/10.1111/bmsp.12321.
Texto completo da fonteLiu, Chen-Wei, Björn Andersson e Anders Skrondal. "Erratum: A Constrained Metropolis–Hastings Robbins–Monro Algorithm for Q Matrix Estimation in DINA Models". Psychometrika, 16 de maio de 2024. http://dx.doi.org/10.1007/s11336-024-09974-5.
Texto completo da fonteGodichon-Baggioni, Antoine, Nicklas Werge e Olivier Wintenberger. "Non-asymptotic analysis of stochastic approximation algorithms for streaming data". ESAIM: Probability and Statistics, 6 de março de 2023. http://dx.doi.org/10.1051/ps/2023006.
Texto completo da fonteLiu, Tianci, Chun Wang e Gongjun Xu. "Estimating three- and four-parameter MIRT models with importance-weighted sampling enhanced variational auto-encoder". Frontiers in Psychology 13 (15 de agosto de 2022). http://dx.doi.org/10.3389/fpsyg.2022.935419.
Texto completo da fonteHuang, Sijia, Jinwen (Jevan) Luo e Li Cai. "An Explanatory Multidimensional Random Item Effects Rating Scale Model". Educational and Psychological Measurement, 13 de dezembro de 2022, 001316442211409. http://dx.doi.org/10.1177/00131644221140906.
Texto completo da fonteHuang, Sijia, e Li Cai. "Cross-Classified Item Response Theory Modeling With an Application to Student Evaluation of Teaching". Journal of Educational and Behavioral Statistics, 24 de agosto de 2023. http://dx.doi.org/10.3102/10769986231193351.
Texto completo da fonteAmini Tehrani, Hamed, Ali Bakhshi e Tony T. Y. Yang. "Online jointly estimation of hysteretic structures using the combination of central difference Kalman filter and Robbins–Monro technique". Journal of Vibration and Control, 12 de maio de 2020, 107754632092560. http://dx.doi.org/10.1177/1077546320925604.
Texto completo da fonteHuang, Sijia, e Dubravka Svetina Valdivia. "Wald χ2 Test for Differential Item Functioning Detection with Polytomous Items in Multilevel Data". Educational and Psychological Measurement, 11 de julho de 2023. http://dx.doi.org/10.1177/00131644231181688.
Texto completo da fonteHuang, Sijia, Seungwon Chung e Carl F. Falk. "Modeling Response Styles in Cross‐Classified Data Using a Cross‐Classified Multidimensional Nominal Response Model". Journal of Educational Measurement, 31 de maio de 2024. http://dx.doi.org/10.1111/jedm.12401.
Texto completo da fonteChung, Seungwon, e Li Cai. "Cross-Classified Random Effects Modeling for Moderated Item Calibration". Journal of Educational and Behavioral Statistics, 12 de janeiro de 2021, 107699862098390. http://dx.doi.org/10.3102/1076998620983908.
Texto completo da fonteWang, Xue, Jing Lu e Jiwei Zhang. "A Metropolis–Hastings Robbins–Monro algorithm via variational inference for estimating the multidimensional graded response model: a calculationally efficient estimation scheme to deal with complex test structures". Computational Statistics, 29 de julho de 2024. http://dx.doi.org/10.1007/s00180-024-01533-x.
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