Likelihood Subgradient Densities
似然次梯度密度
We introduce likelihood subgradient densities and explore their basic properties. Using mixtures of likelihood subgradient densities, we propose an approach for constructing tight enveloping functions in the Bayesian context. In the case of normal priors with normal data, the area underneath the resulting enveloping function is bounded above by . The approach is extended to k-dimensional models where the corresponding bound is . More generally, our approach should also yield…