Abstract
Variational Bayesian Approximation (VBA) methods are recent tools for effective full Bayesian computations. In this paper, these tools are used for linear inverse problems where the prior models include hidden variables (Hierarchical prior models) and where the estimation of the hyper parameters has also to be addressed. In particular one specific prior model (Student-t) is considered and used via a hierarchical representation with hidden variables and the details of the resulted VBA algorithms are given.
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Mohammad-Djafari, A. (2013). Variational Bayesian Approximation for Linear Inverse Problems with a Hierarchical Prior Models. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2013. Lecture Notes in Computer Science, vol 8085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40020-9_74
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DOI: https://doi.org/10.1007/978-3-642-40020-9_74
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