
Chapter 6
Conditional Inferential Models
Portions of the material in this chapter are from R. Martin and C. Liu, “Condi-
tional inferential models: combining information for prior-free probabilistic infer-
ence,” Journal of the Royal Statistical Society Series B 77, 195–217, 2015, reprinted
with permission by John Wiley & Sons, Inc.
6.1 Introduction
The basic inferential model (IM) approach presented in Chapter 4 provides a gen-
eral framework for valid prior-free probabilistic inference. The key idea is, first, to
associate observable data X, unknown parameter θ , and an unobservable auxiliary
variable U and, second, to predict the unobserved value