Works (3)

Updated: August 28th, 2023 21:16

2022 journal article

Geostatistical modeling of positive-definite matrices: An application to diffusion tensor imaging

BIOMETRICS, 78(2), 548–559.

By: Z. Lan*, B. Reich, J. Guinness, D. Bandyopadhyay, L. Ma & F. Moeller

author keywords: Cholesky decomposition; diffusion tensor imaging; geostatistical modeling; positive‐ definite matrix; spatial random fields; spatial Wishart process
MeSH headings : Computer Simulation; Diffusion Tensor Imaging; Normal Distribution; Stochastic Processes
TL;DR: An approximation method is proposed to obtain a feasible Cholesky decomposition model, which is shown to be asymptotically equivalent to the spatial Wishart process model and to produce reliable inference and improved performance, compared to other methods. (via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being (Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: March 8, 2021

2021 journal article

A spatial Bayesian semiparametric mixture model for positive definite matrices with applications in diffusion tensor imaging


By: Z. Lan n, B. Reich n & D. Bandyopadhyay*

author keywords: Diffusion tensor imaging; inverse Wishart distribution; matrix variate; positive definite matrix; spatial statistics
TL;DR: A matrix‐variate Bayesian semiparametric mixture model is proposed, where the p.d. matrices are distributed as a mixture of inverse Wishart distributions, with the spatial dependence captured by a Markov model for the mixture component labels. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: February 15, 2021

2016 journal article

Bayesian network feature finder (BANFF): an r package for gene network feature selection

Bioinformatics, 32(23), 3685–3687.

By: Z. Lan, Y. Zhao, J. Kang & T. Yu

Source: NC State University Libraries
Added: August 6, 2018

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