Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Chakrabarty Dalia"'
Publikováno v:
International Journal of Metrology and Quality Engineering, Vol 15, p 4 (2024)
Policy decisions are often motivated by results attained by a cohort of responders to a survey or a test. However, erroneous identification of the reliability or the complimentary uncertainty of the test/survey instrument, will distort the data that
Externí odkaz:
https://doaj.org/article/c3172b93e1a849c59efd3bc64fa550b4
Publikováno v:
Artificial Intelligence in Medicine, 2024
Patients who are undergoing physical rehabilitation, benefit from feedback that follows from reliable assessment of their cumulative performance attained at a given time. In this paper, we provide a method for the learning of the recovery trajectory
Externí odkaz:
http://arxiv.org/abs/2410.21983
Autor:
Roy, Gargi, Chakrabarty, Dalia
We present a new strategy for learning the functional relation between a pair of variables, while addressing inhomogeneities in the correlation structure of the available data, by modelling the sought function as a sample function of a non-stationary
Externí odkaz:
http://arxiv.org/abs/2404.12478
Autor:
Wang, Kangrui, Chakrabarty, Dalia
We present a new method for learning Soft Random Geometric Graphs (SRGGs), drawn in probabilistic metric spaces, with the connection function of the graph defined as the marginal posterior probability of an edge random variable, given the correlation
Externí odkaz:
http://arxiv.org/abs/2002.01339
Autor:
Chakrabarty, Dalia
Publikováno v:
In Journal of the Franklin Institute February 2023 360(3):1635-1671
Autor:
Spire, Cedric, Chakrabarty, Dalia
There are multiple real-world problems in which training data is unavailable, and still, the ambition is to learn values of the system parameters, at which test data on an observable is realised, subsequent to the learning of the functional relations
Externí odkaz:
http://arxiv.org/abs/1811.09204
Autor:
Wang, Kangrui, Chakrabarty, Dalia
We undertake Bayesian learning of the high-dimensional functional relationship between a system parameter vector and an observable, that is in general tensor-valued. The ultimate aim is Bayesian inverse prediction of the system parameters, at which t
Externí odkaz:
http://arxiv.org/abs/1803.04582
Autor:
Chakrabarty, Dalia1 (AUTHOR) dalia.chakrabarty@brunel.ac.uk, Wang, Kangrui2 (AUTHOR), Roy, Gargi1 (AUTHOR), Bhojgaria, Akash3 (AUTHOR), Zhang, Chuqiao1 (AUTHOR), Pavlu, Jiri4 (AUTHOR), Chakrabartty, Joydeep3 (AUTHOR)
Publikováno v:
PLoS ONE. 10/19/2023, Vol. 18 Issue 10, p1-28. 28p.
Autor:
Wang, Kangrui, Chakrabarty, Dalia
We present a method for simultaneous Bayesian learning of the correlation matrix and graphical model of a multivariate dataset, along with uncertainties in each, to subsequently compute distance between the learnt graphical models of a pair of datase
Externí odkaz:
http://arxiv.org/abs/1710.11292