Relevant bibliographies by topics / Algorithme de Robbins-Monro

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Algorithme de Robbins-Monro'

Author: Grafiati
Published: 21 September 2024
Last updated: 31 July 2025

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Journal articles on the topic "Algorithme de Robbins-Monro"

1

Moler, José A., Fernando Plo, and Miguel San Miguel. "Adaptive designs and Robbins–Monro algorithm." Journal of Statistical Planning and Inference 131, no. 1 (2005): 161–74. http://dx.doi.org/10.1016/j.jspi.2003.12.018.

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Arouna, Bouhari. "Robbins–Monro algorithms and variance reduction in finance." Journal of Computational Finance 7, no. 2 (2003): 35–61. http://dx.doi.org/10.21314/jcf.2003.111.

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XU, ZI, YINGYING LI, and XINGFANG ZHAO. "SIMULATION-BASED OPTIMIZATION BY NEW STOCHASTIC APPROXIMATION ALGORITHM." Asia-Pacific Journal of Operational Research 31, no. 04 (2014): 1450026. http://dx.doi.org/10.1142/s0217595914500262.

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This paper proposes one new stochastic approximation algorithm for solving simulation-based optimization problems. It employs a weighted combination of two independent current noisy gradient measurements as the iterative direction. It can be regarded as a stochastic approximation algorithm with a special matrix step size. The almost sure convergence and the asymptotic rate of convergence of the new algorithm are established. Our numerical experiments show that it outperforms the classical Robbins–Monro (RM) algorithm and several other existing algorithms for one noisy nonlinear function minimi
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Wardi, Y. "On a proof of a Robbins-Monro algorithm." Journal of Optimization Theory and Applications 64, no. 1 (1990): 217. http://dx.doi.org/10.1007/bf00940033.

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Lin, Siming, and Jennie Si. "Weight-Value Convergence of the SOM Algorithm for Discrete Input." Neural Computation 10, no. 4 (1998): 807–14. http://dx.doi.org/10.1162/089976698300017485.

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Some insights on the convergence of the weight values of the self-organizing map (SOM) to a stationary state in the case of discrete input are provided. The convergence result is obtained by applying the Robbins-Monro algorithm and is applicable to input-output maps of any dimension.
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6

Moser, Barry Kurt, and Melinda H. McCann. "Algorithm AS 316: A Robbins-Monro-based Sequential Procedure." Journal of the Royal Statistical Society: Series C (Applied Statistics) 46, no. 3 (1997): 388–99. http://dx.doi.org/10.1111/1467-9876.00078.

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El Moumen, AbdelKader, Salim Benslimane, and Samir Rahmani. "Robbins–Monro Algorithm with $$\boldsymbol{\psi}$$-Mixing Random Errors." Mathematical Methods of Statistics 31, no. 3 (2022): 105–19. http://dx.doi.org/10.3103/s1066530722030024.

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8

Cai, Li. "Metropolis-Hastings Robbins-Monro Algorithm for Confirmatory Item Factor Analysis." Journal of Educational and Behavioral Statistics 35, no. 3 (2010): 307–35. http://dx.doi.org/10.3102/1076998609353115.

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9

Chen, Han-Fu. "Stochastic approximation with non-additive measurement noise." Journal of Applied Probability 35, no. 2 (1998): 407–17. http://dx.doi.org/10.1239/jap/1032192856.

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The Robbins–Monro algorithm with randomly varying truncations for measurements with non-additive noise is considered. Assuming that the function under observation is locally Lipschitz-continuous in its first argument and that the noise is a φ-mixing process, strong consistency of the estimate is shown. Neither growth rate restriction on the function, nor the decreasing rate of the mixing coefficients are required.
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10

Chen, Han-Fu. "Stochastic approximation with non-additive measurement noise." Journal of Applied Probability 35, no. 02 (1998): 407–17. http://dx.doi.org/10.1017/s0021900200015035.

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The Robbins–Monro algorithm with randomly varying truncations for measurements with non-additive noise is considered. Assuming that the function under observation is locally Lipschitz-continuous in its first argument and that the noise is a φ-mixing process, strong consistency of the estimate is shown. Neither growth rate restriction on the function, nor the decreasing rate of the mixing coefficients are required.
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Dissertations / Theses on the topic "Algorithme de Robbins-Monro"

1

Lu, Wei. "Μéthοdes stοchastiques du secοnd οrdre pοur le traitement séquentiel de dοnnées massives". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMIR13.

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Avec le développement rapide des technologies et l'acquisition de données de plus en plus massives, les méthodes capables de traiter les données de manière séquentielle (en ligne) sont devenues indispensables. Parmi ces méthodes, les algorithmes de gradient stochastique se sont imposés pour estimer le minimiseur d'une fonction exprimée comme l'espérance d'une fonction aléatoire. Bien qu'ils soient devenus incontournables, ces algorithmes rencontrent des difficultés lorsque le problème est mal conditionné. Dans cette thèse, nous nous intéressons sur les algorithmes stochastiques du second ordre
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Arouna, Bouhari. "Méthodes de Monté Carlo et algorithmes stochastiques." Marne-la-vallée, ENPC, 2004. https://pastel.archives-ouvertes.fr/pastel-00001269.

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Hajji, Kaouther. "Accélération de la méthode de Monte Carlo pour des processus de diffusions et applications en Finance." Thesis, Paris 13, 2014. http://www.theses.fr/2014PA132054/document.

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Dans cette thèse, on s’intéresse à la combinaison des méthodes de réduction de variance et de réduction de la complexité de la méthode Monte Carlo. Dans une première partie de cette thèse, nous considérons un modèle de diffusion continu pour lequel on construit un algorithme adaptatif en appliquant l’importance sampling à la méthode de Romberg Statistique Nous démontrons un théorème central limite de type Lindeberg Feller pour cet algorithme. Dans ce même cadre et dans le même esprit, on applique l’importance sampling à la méthode de Multilevel Monte Carlo et on démontre également un théorème
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Stanley, Leanne M. "Flexible Multidimensional Item Response Theory Models Incorporating Response Styles." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1494316298549437.

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Conference papers on the topic "Algorithme de Robbins-Monro"

1

Ram, S. Sundhar, V. V. Veeravalli, and A. Nedic. "Incremental Robbins-Monro Gradient Algorithm for Regression in Sensor Networks." In 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. IEEE, 2007. http://dx.doi.org/10.1109/camsap.2007.4498027.

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2

Iooss, Bertrand, and Jérôme Lonchampt. "Robust Tuning of Robbins-Monro Algorithm for Quantile Estimation -- Application to Wind-Farm Asset Management." In Proceedings of the 31st European Safety and Reliability Conference. Research Publishing Services, 2021. http://dx.doi.org/10.3850/978-981-18-2016-8_084-cd.

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You might also be interested in the extended bibliographies on the topic 'Algorithme de Robbins-Monro' for particular source types:
Journal articles
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