AI RESEARCH
Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators
arXiv CS.LG
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ArXi:2601.20888v3 Announce Type: replace-cross We study sampling from posterior distributions in Bayesian linear inverse problems where $A$, the parameters to observables operator, is computationally expensive. In many applications, $A$ can be factored in a manner that facilitates the construction of a cost-effective approximation $\tilde{A}$. In this framework, we