Inferential models (IMs) offer reliable, data-driven, possibilistic statistical inference. But despite IMs' theoretical/foundational advantages, efficient computation in applications is a major challenge. This paper presents a simple and apparently powerful Monte Carlo-driven strategy for approximating the IM's possibility contour, or at least its $\alpha$-level set for a specified $\alpha$. Our proposal utilizes a parametric family that, in a certain sense, approximately covers the credal set associated with the IM's possibility measure, which is reminiscent of variational approximations now widely used in Bayesian statistics.

翻译：推理模型（IMs）提供了可靠、数据驱动的可能性统计推断。然而，尽管IMs具有理论/基础优势，在应用中的高效计算仍是一个重大挑战。本文提出了一种简单且看似强大的蒙特卡洛驱动策略，用于近似IM的可能性轮廓，或至少针对指定α值近似其α水平集。我们的方案利用了一个参数族，在某种意义上该参数族近似覆盖了与IM的可能性度量相关的信念集，这令人联想到目前广泛应用于贝叶斯统计中的变分近似方法。