Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Kody J H Law"'
Publikováno v:
New Journal of Physics, Vol 22, Iss 6, p 063038 (2020)
Bayesian inference is a powerful paradigm for quantum state tomography, treating uncertainty in meaningful and informative ways. Yet the numerical challenges associated with sampling from complex probability distributions hampers Bayesian tomography
Externí odkaz:
https://doaj.org/article/e9533fed90ee41c99d80218bee8f4452
Publikováno v:
npj Quantum Information, Vol 7, Iss 1, Pp 1-10 (2021)
Abstract The method of classical shadows proposed by Huang, Kueng, and Preskill heralds remarkable opportunities for quantum estimation with limited measurements. Yet its relationship to established quantum tomographic approaches, particularly those
Externí odkaz:
https://doaj.org/article/695587a97081475592b7614e589976d5
Publikováno v:
Sensors, Vol 22, Iss 16, p 6112 (2022)
Cyber-physical system security presents unique challenges to conventional measurement science and technology. Anomaly detection in software-assisted physical systems, such as those employed in additive manufacturing or in DNA synthesis, is often hamp
Externí odkaz:
https://doaj.org/article/6c5c26081f5d487cae2c878becef4cb0
Autor:
Kody J. H. Law, Vitaly Zankin
Publikováno v:
SIAM/ASA Journal on Uncertainty Quantification. 10:1070-1100
This work considers variational Bayesian inference as an inexpensive and scalable alternative to a fully Bayesian approach in the context of sparsity-promoting priors. In particular, the priors considered arise from scale mixtures of Normal distribut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af3b366de182676d2cd2fc6b54e2c13f
https://doi.org/10.1137/21m1401188
https://doi.org/10.1137/21m1401188
Publikováno v:
Advances in Applied Probability. 54:661-687
In this article we consider a Monte-Carlo-based method to filter partially observed diffusions observed at regular and discrete times. Given access only to Euler discretizations of the diffusion process, we present a new procedure which can return on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69ed005de30b60f9d3b5e55068eae2f4
https://doi.org/10.1017/apr.2021.50
https://doi.org/10.1017/apr.2021.50
Publikováno v:
International Journal for Uncertainty Quantification. 9:321-330
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eadc38e036e3650a82ae75c33b6ee81e
https://doi.org/10.1615/int.j.uncertaintyquantification.2019027245
https://doi.org/10.1615/int.j.uncertaintyquantification.2019027245
Autor:
Mark Cianciosa, David E. Bernholdt, Jin M. Park, Clement Etienam, David Green, Kody J. H. Law
Publikováno v:
Foundations of Data Science. 1:491-506
This paper presents a method for solving the supervised learning problem in which the output is highly nonlinear and discontinuous. It is proposed to solve this problem in three stages: (ⅰ) cluster the pairs of input-output data points, resulting i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::30db47ad54a22446c1ca523916858830
https://doi.org/10.3934/fods.2019020
https://doi.org/10.3934/fods.2019020
Autor:
Farzana Nasrin, David J. Keffer, Louis J. Santodonato, Adam Spannaus, Vasileios Maroulas, Piotr Luszczek, Peter K. Liaw, Cassie Putman Micucci, Kody J. H. Law
Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates. Methods for visualizing the local atomic structure, such a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79ce6dce92319096e6c9f5bc203d5b8a
http://arxiv.org/abs/2101.05808
http://arxiv.org/abs/2101.05808
Publikováno v:
Conference on Lasers and Electro-Optics.
Classical shadows enable remarkably efficient estimation of quantum observables, yet their connection to conventional techniques is unclear. In simulated examples we show that Bayesian mean estimation attains lower error on average, whereas classical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b7a1e025db6658cab20539490bb6d3d
https://doi.org/10.1364/cleo_qels.2021.fw3n.3
https://doi.org/10.1364/cleo_qels.2021.fw3n.3
In this article, we consider computing expectations w.r.t. probability measures which are subject to discretization error. Examples include partially observed diffusion processes or inverse problems, where one may have to discretize time and/or space
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::416caeaa77c8ad89c919bfef07c250ae