Design of Compressed Sensing Systems via Density-Evolution Framework for Structure Recovery in Graphical Models. (arXiv:2203.09636v1 [cs.IT])

It has been shown that the task of learning the structure of Bayesian
networks (BN) from observational data is an NP-Hard problem. Although there
have been attempts made to tackle this problem, these solutions assume direct
access to the observational data which may not be practical in certain
applications. In this paper, we explore the feasibility of recovering the
structure of Gaussian Bayesian Network (GBN) from compressed (low dimensional
and indirect) measurements. We propose a novel density-evolution based
framework for …

arxiv design evolution framework sensing systems