Paper detail

On Existence Theorems for Conditional Inferential Models

The framework of Inferential Models (IMs) has recently been developed in search of what is referred to as the holy grail of statistical theory, that is, prior-free probabilistic inference. Its method of Conditional IMs (CIMs) is a critical component in that it serves as a desirable extension of the Bayes theorem for combining information when no prior distribution is available. The general form of CIMs is defined by a system of first-order homogeneous linear partial differential equations (PDEs). When admitting simple solutions, they are referred to as regular, whereas when no regular CIMs exist, they are used as the so-called local CIMs. This paper provides conditions for regular CIMs, which are shown to be equivalent to the existence of a group-theoretical representation of the underlying statistical model. It also establishes existence theorems for CIMs, which state that under mild conditions, local CIMs always exist. Finally, the paper concludes with a simple example and a few remarks on future developments of CIMs for applications to popular but inferentially nontrivial statistical models.