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pro vyhledávání: '"Shojaie A"'
This paper studies the problem of learning Bayesian networks from continuous observational data, generated according to a linear Gaussian structural equation model. We consider an $\ell_0$-penalized maximum likelihood estimator for this problem which
Externí odkaz:
http://arxiv.org/abs/2408.11977
Autor:
Cheng, Si, Blanco, Magali N., Larson, Timothy V., Sheppard, Lianne, Szpiro, Adam, Shojaie, Ali
Dimension reduction is often the first step in statistical modeling or prediction of multivariate spatial data. However, most existing dimension reduction techniques do not account for the spatial correlation between observations and do not take the
Externí odkaz:
http://arxiv.org/abs/2408.01662
Exposure assessment is fundamental to air pollution cohort studies. The objective is to predict air pollution exposures for study subjects at locations without data in order to optimize our ability to learn about health effects of air pollution. In a
Externí odkaz:
http://arxiv.org/abs/2406.01982
We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art structure learning methods for this setting have at least one of the fol
Externí odkaz:
http://arxiv.org/abs/2404.12592
Autor:
Shojaie, Ali, Chen, Wenyu
Directed acyclic graphs (DAGs) are commonly used to model causal relationships among random variables. In general, learning the DAG structure is both computationally and statistically challenging. Moreover, without additional information, the directi
Externí odkaz:
http://arxiv.org/abs/2403.16031
Advances in modern technology have enabled the simultaneous recording of neural spiking activity, which statistically can be represented by a multivariate point process. We characterise the second order structure of this process via the spectral dens
Externí odkaz:
http://arxiv.org/abs/2403.12908
Motivated by the problem of inferring the graph structure of functional connectivity networks from multi-level functional magnetic resonance imaging data, we develop a valid inference framework for high-dimensional graphical models that accounts for
Externí odkaz:
http://arxiv.org/abs/2403.10034
A discrete spatial lattice can be cast as a network structure over which spatially-correlated outcomes are observed. A second network structure may also capture similarities among measured features, when such information is available. Incorporating t
Externí odkaz:
http://arxiv.org/abs/2401.15793
Doubly-stochastic point processes model the occurrence of events over a spatial domain as an inhomogeneous Poisson process conditioned on the realization of a random intensity function. They are flexible tools for capturing spatial heterogeneity and
Externí odkaz:
http://arxiv.org/abs/2306.06756
Autor:
Hassan Soleimanpour, Saber Ghaffari-fam, Ehsan Sarbazi, Raheleh Gholami, Hosein Azizi, Masumeh Daliri, Saman Sedighi, Hossein-Ali Nikbakht, Layla Shojaie, Ali Allahyari
Publikováno v:
Journal of Rehabilitation Sciences and Research, Vol 11, Iss 4, Pp 196-201 (2024)
Background: The growing number of aging people, ensuring their quality of life (QoL), and the social services designed for this population group are becoming increasingly significant concerns. This study explored how socioeconomic status and self-car
Externí odkaz:
https://doaj.org/article/2326bab8c7444c1babd49b8ef60f8876
