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  1. Graph signal recovery using variational Bayes in Fourier pairs with Cramér–Rao bounds

Graph signal recovery using variational Bayes in Fourier pairs with Cramér–Rao bounds

Torkamani, R ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.1016/j.sigpro.2024.109394
  3. Publisher: 2024
  4. Abstract:
  5. In this paper, the graph signal recovery problem is addressed by employing an aggregation of samples in the vertex domain and the Fourier graph transform domain. The statistical graph signal is modeled using a Gaussian Markov Random Field (GMRF). The reconstruction process involves employing a variational Bayes (VB) algorithm, which is a fully Bayesian method that iteratively estimates all unknown parameters by computing the posteriors in a closed-form. Furthermore, the closed-form of the Cramér–Rao lower bound (CRLB) for graph signal estimation is also derived. Simulation results demonstrate the superiority of the proposed algorithm over some of state-of-the-art algorithms in the literature. © 2024 Elsevier B.V
  6. Keywords:
  7. Cramér–Rao lower bound ; Fourier pairs ; Barium compounds ; Bayesian networks ; Fourier transforms ; Gaussian noise (electronic) ; Markov processes ; Recovery ; Signal reconstruction ; Closed form ; Crame Rao bounds ; Crame-Rao lower bounds ; Cramer Rao lower bound ; Fourier ; Fourier pair ; Graph signal recovery ; Signal recovery ; Variational bayes ; Variational bayesian ; Iterative methods
  8. Source: Signal Processing ; Volume 219 , 2024 ; 01651684 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0165168424000136