Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Éric Moulines"'
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2017, Iss 1, Pp 1-15 (2017)
Abstract This paper focuses on sequential Monte Carlo approximations of smoothing distributions in conditionally linear and Gaussian state spaces. To reduce Monte Carlo variance of smoothers, it is typical in these models to use Rao-Blackwellization:
Externí odkaz:
https://doaj.org/article/f98ef289135a4b748c7db4a78e499dc8
Publikováno v:
SIAM Review. 64:991-1028
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::37ef33a91b598cd35ae8439498b9d247
https://doi.org/10.1137/22m1522917
https://doi.org/10.1137/22m1522917
Methane emissions are the second leading cause of global warming. Because of the near-term warming potential of atmospheric methane, reducing its emissions will be essential to achieve the UNFCCC climate objectives. Reducing methane emissions from oi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::43e51f036033c82f5f2a120c771fbd43
https://doi.org/10.5194/egusphere-egu23-15237
https://doi.org/10.5194/egusphere-egu23-15237
Autor:
Denis Belomestny, Cristina Butucea, Enno Mammen, Eric Moulines, Markus Reiß, Vladimir V. Ulyanov
This book contains contributions from the participants of the international conference “Foundations of Modern Statistics” which took place at Weierstrass Institute for Applied Analysis and Stochastics (WIAS), Berlin, during November 6–8, 2019,
Publikováno v:
Mathematical Programming
Mathematical Programming, 2023, 199, pp.793-830. ⟨10.1007/s10107-022-01850-3⟩
Unifying mirror descent and dual averaging, arXiv(2019)
Mathematical Programming, 2023, 199, pp.793-830. ⟨10.1007/s10107-022-01850-3⟩
Unifying mirror descent and dual averaging, arXiv(2019)
We introduce and analyze a new family of first-order optimization algorithms which generalizes and unifies both mirror descent and dual averaging. Within the framework of this family, we define new algorithms for constrained optimization that combine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47b3d933b6132b905bfe9a3d77630190
http://arxiv.org/abs/1910.13742
http://arxiv.org/abs/1910.13742
Publikováno v:
SSP 2018-IEEE Statistical Signal Processing Workshop
SSP 2018-IEEE Statistical Signal Processing Workshop, Jun 2018, Freiburg, Germany
IEEE Statistical Signal Processing Workshop
IEEE Statistical Signal Processing Workshop, 2018
HAL
SSP 2018-IEEE Statistical Signal Processing Workshop, Jun 2018, Freiburg, Germany
IEEE Statistical Signal Processing Workshop
IEEE Statistical Signal Processing Workshop, 2018
HAL
International audience
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::94da50bfa5c655fd17278dae9d3f5498
https://hal.science/hal-01710321
https://hal.science/hal-01710321
This book covers the classical theory of Markov chains on general state-spaces as well as many recent developments. The theoretical results are illustrated by simple examples, many of which are taken from Markov Chain Monte Carlo methods. The book is
Publikováno v:
Monte Carlo Methods and Applications
Monte Carlo Methods and Applications, De Gruyter, 2017
HAL
Monte Carlo Methods and Applications, De Gruyter, 2017
HAL
International audience
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::40a6f448e3eb40c7e638954afaeacc9a
https://hal.archives-ouvertes.fr/hal-01711748
https://hal.archives-ouvertes.fr/hal-01711748
Publikováno v:
Proceedings of Machine Learning Research
Proceedings of Machine Learning Research, 2017, 65, pp.319-342
HAL
Proceedings of Machine Learning Research, PMLR, 2017, 65, pp.319-342
Proceedings of Machine Learning Research, 2017, 65, pp.319-342
HAL
Proceedings of Machine Learning Research, PMLR, 2017, 65, pp.319-342
This paper presents a detailed theoretical analysis of the Langevin Monte Carlo sampling algorithm recently introduced in Durmus et al. (Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau, 2016) when appli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f0fc13e138ddd0dfc94af3c60dace1da
This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time