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Title
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Statistical Graph Signal Recovery Using Variational Bayes
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Type
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JournalPaper
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Keywords
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Graph signal recovery, Laplacian matrix, sampling, variational bayes, generalized CCH distribution.
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Abstract
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This brief investigates the problem of Graph Signal Recovery (GSR) when the topology of the graph is not known in advance. In this brief, the elements of the weighted adjacency matrix is statistically related to normal distribution and the graph signal is assumed to be Gaussian Markov Random Field (GMRF). Then, the problem of GSR is solved by a Variational Bayes (VB) algorithm in a Bayesian manner by computing the posteriors in a closed form. The posteriors of the elements of weighted adjacency matrix are proved to have a new distribution which we call it Generalized Compound Confluent Hypergeometric (GCCH) distribution. Moreover, the variance of the noise is estimated by calculating its posterior via VB.
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Researchers
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Hadi Zayyani (Second Researcher)
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