DOAJ Open Access 2021

A Global Bayes Factor for Observations on an Infinite-Dimensional Hilbert Space, Applied to Signal Detection in fMRI

Khalil Shafie Mohammad Reza Faridrohani Siamak Noorbaloochi Hossein Moradi Rekabdarkolaee

Abstrak

Functional Magnetic Resonance Imaging (fMRI) is a fundamental tool in advancing our understanding of the brain's functionality. Recently, a series of Bayesian approaches have been suggested to test for the voxel activation in different brain regions. In this paper, we propose a novel definition for the global Bayes factor to test for activation using the Radon-Nikodym derivative. Our proposed method extends the definition of Bayes factor to an infinite dimensional Hilbert space. Using this extended definition, a Bayesian testing procedure is introduced for signal detection in noisy images when both signal and noise are considered as an element of an infinite dimensional Hilbert space. This new approach is illustrated through a real data analysis to find activated areas of Brain in an fMRI data.

Topik & Kata Kunci

Probabilities. Mathematical statistics Statistics

Penulis (4)

Khalil Shafie

Mohammad Reza Faridrohani

Siamak Noorbaloochi

Hossein Moradi Rekabdarkolaee

Format Sitasi

APA MLA BibTeX

Shafie, K., Faridrohani, M.R., Noorbaloochi, S., Rekabdarkolaee, H.M. (2021). A Global Bayes Factor for Observations on an Infinite-Dimensional Hilbert Space, Applied to Signal Detection in fMRI. https://doi.org/10.17713/ajs.v50i3.1050

Akses Cepat

PDF tidak tersedia langsung

Cek di sumber asli →

Lihat di Sumber doi.org/10.17713/ajs.v50i3.1050

Informasi Jurnal

Tahun Terbit: 2021
Sumber Database: DOAJ
DOI: 10.17713/ajs.v50i3.1050
Akses: Open Access ✓