Zobrazeno 1 - 10
of 409
pro vyhledávání: '"Saibaba P"'
In Bayesian inverse problems, it is common to consider several hyperparameters that define the prior and the noise model that must be estimated from the data. In particular, we are interested in linear inverse problems with additive Gaussian noise an
Externí odkaz:
http://arxiv.org/abs/2412.02773
Autor:
Ipsen, Ilse C. F., Saibaba, Arvind K.
The notion of `stable rank' of a matrix is central to the analysis of randomized matrix algorithms, covariance estimation, deep neural networks, and recommender systems. We compare the properties of the stable rank and intrinsic dimension of real and
Externí odkaz:
http://arxiv.org/abs/2407.21594
Autor:
Khan, Abraham, Saibaba, Arvind K.
Computing low-rank approximations of kernel matrices is an important problem with many applications in scientific computing and data science. We propose methods to efficiently approximate and store low-rank approximations to kernel matrices that depe
Externí odkaz:
http://arxiv.org/abs/2406.06344
This paper tackles optimal sensor placement for Bayesian linear inverse problems, a popular version of the more general Optimal Experiment Design (OED) problem, using the D-optimality criterion. This is done by establishing connections between sensor
Externí odkaz:
http://arxiv.org/abs/2402.16000
Strong Constraint 4D Variational (SC-4DVAR) is a data assimilation method that is widely used in climate and weather applications. SC-4DVAR involves solving a minimization problem to compute the maximum a posteriori estimate, which we tackle using th
Externí odkaz:
http://arxiv.org/abs/2401.15758
We study Bayesian methods for large-scale linear inverse problems, focusing on the challenging task of hyperparameter estimation. Typical hierarchical Bayesian formulations that follow a Markov Chain Monte Carlo approach are possible for small proble
Externí odkaz:
http://arxiv.org/abs/2311.15827
Publikováno v:
Geoscientific Model Development, Vol 17, Pp 8853-8872 (2024)
Inverse models arise in various environmental applications, ranging from atmospheric modeling to geosciences. Inverse models can often incorporate predictor variables, similar to regression, to help estimate natural processes or parameters of interes
Externí odkaz:
https://doaj.org/article/e38feb6a2944429ab0c177f2df7d3772
Autor:
Sheerin Anjum, Neelam N. Sreedevi, Sandeep Mahapatra, Anusha Aripikatla, Siraj Ahmed Khan, Noorjahan Mohammed, Madrol Vijayabhaskar, Kompella S.S. Saibaba
Publikováno v:
Indian Journal of Vascular and Endovascular Surgery, Vol 11, Iss 3, Pp 174-179 (2024)
Background: Venous thrombosis is a common life-threatening disorder and is a significant source of morbidity and mortality in hospitalized patients. This study aims to evaluate soluble P-selectin (sP-Sel) and to correlate with D-dimer and C-reactive
Externí odkaz:
https://doaj.org/article/69ed79e54b024a1cbc62bbd3047bb668
Autor:
Saibaba, Arvind K., Międlar, Agnieszka
This paper expands the analysis of randomized low-rank approximation beyond the Gaussian distribution to four classes of random matrices: (1) independent sub-Gaussian entries, (2) independent sub-Gaussian columns, (3) independent bounded columns, and
Externí odkaz:
http://arxiv.org/abs/2308.05814
Autor:
Antil, Harbir, Saibaba, Arvind K.
This paper is interested in developing reduced order models (ROMs) for repeated simulation of fractional elliptic partial differential equations (PDEs) for multiple values of the parameters (e.g., diffusion coefficients or fractional exponent) govern
Externí odkaz:
http://arxiv.org/abs/2306.16148