Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Alessandro Barp"'
Autor:
Anthony Baptista, Alessandro Barp, Tapabrata Chakraborti, Chris Harbron, Ben D. MacArthur, Christopher R. S. Banerji
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Deep neural networks (DNNs) are powerful tools for approximating the distribution of complex data. It is known that data passing through a trained DNN classifier undergoes a series of geometric and topological simplifications. While some pro
Externí odkaz:
https://doaj.org/article/97e8c4f8b80743829e934cc3478f8c28
Autor:
Andreas Anastasiou, Alessandro Barp, François-Xavier Briol, Bruno Ebner, Robert E. Gaunt, Fatemeh Ghaderinezhad, Jackson Gorham, Arthur Gretton, Christophe Ley, Qiang Liu, Lester Mackey, Chris J. Oates, Gesine Reinert, Yvik Swan
Publikováno v:
Statistical Science. 38
Stein’s method compares probability distributions through the study of a class of linear operators called Stein operators. While mainly studied in probability and used to underpin theoretical statistics, Stein’s method has led to significant adva
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b6eb63a1e41f143700cb38b55b8a842
https://doi.org/10.1214/22-sts863
https://doi.org/10.1214/22-sts863
Publikováno v:
Bernoulli. 28
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6af1398680dec4e0e236c2602f68a105
https://doi.org/10.3150/21-bej1415
https://doi.org/10.3150/21-bej1415
Autor:
Alessandro Barp
Publikováno v:
Springer Proceedings in Mathematics & Statistics ISBN: 9783030779566
SPIGL
SPIGL
Many of the state-of-the-art Monte Carlo sampling algorithms are inspired by Hamiltonian Monte Carlo and constructed from measure-preserving Langevin diffusions through an appropriate geometric integrator. In this article, following [9] we discuss th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7a4a23bf407e0cf3ac022045dcb045f7
https://doi.org/10.1007/978-3-030-77957-3_18
https://doi.org/10.1007/978-3-030-77957-3_18
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030269791
GSI
GSI
It is well-known that irreversible MCMC algorithms converge faster to their stationary distributions than reversible ones. Using the special geometric structure of Lie groups \(\mathcal G\) and dissipation fields compatible with the symplectic struct
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e6615da67d2142b3a359f3ab0671765
https://doi.org/10.1007/978-3-030-26980-7_18
https://doi.org/10.1007/978-3-030-26980-7_18
Autor:
Alessandro Barp
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030269791
GSI
GSI
In this paper we show that the Hamiltonian Monte Carlo method for compact Lie groups constructed in [20] using a symplectic structure can be recovered from canonical geometric mechanics with a bi-invariant metric. Hence we obtain the correspondence b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::245c24110c6ad9c76cc5613f8fccd539
https://doi.org/10.1007/978-3-030-26980-7_69
https://doi.org/10.1007/978-3-030-26980-7_69
Markov Chain Monte Carlo methods have revolutionised mathematical computation and enabled statistical inference within many previously intractable models. In this context, Hamiltonian dynamics have been proposed as an efficient way of building chains
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db2f905447d2ea0954091a243965f02d
http://arxiv.org/abs/1705.02891
http://arxiv.org/abs/1705.02891
We have studied numerically the random interchange model and related loop models on the three-dimensional cubic lattice. We have determined the transition time for the occurrence of long loops. The joint distribution of the lengths of long loops is P
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea43e44d1de11377a08417c1268f2e48
http://arxiv.org/abs/1505.00983
http://arxiv.org/abs/1505.00983
Publikováno v:
Journal of Physics A: Mathematical & Theoretical; 8/27/2015, Vol. 48 Issue 34, p1-1, 1p