Bayesian Inference Using the Proximal Mapping: Uncertainty Quantification Under Varying Dimensionality
利用近端映射开展贝叶斯推断：维数变化下的不确定性量化
In statistical applications, it is common to encounter parameters supported on a varying or unknown dimensional space. Examples include the fused lasso regression, the matrix recovery under an unknown low rank, etc. Despite the ease of obtaining a point estimate via optimization, it is much more challenging to quantify their uncertainty. In the Bayesian framework, a major difficulty is that if assigning the prior associated with a p-dimensional measure, then there is zero…