Variational approximations of possibilistic inferential models

Autor:	Cella, Leonardo, Martin, Ryan
Rok vydání:	2024
Předmět:	Statistics - Computation Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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. Comment: Comments welcome at https://researchers.one/articles/24.04.00005
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.19224 Zobrazit plný text záznamu View this record from Arxiv