Designing truncated priors for direct and inverse Bayesian problems

The Bayesian approach to inverse problems with functional unknowns, has received significant attention in recent years. An important component of the developing theory is study asymptotic performance posterior distribution frequentist setting. present paper contributes area by formulating a contraction for linear problems, truncated Gaussian series priors, and under general smoothness assumptions. Emphasis on intrinsic role truncation point both direct as well problem, which are related through modulus continuity this was recently highlighted Knapik Salomond (2018).