  In this work, we undertake a comprehensive reformulation, modification, and
extension of Smith and Spiegelhalter's (1980) and (1982) Bayes Factor work
within the evolving subject of Objective Bayes Factors. Our primary focus
centers on defining and computing empirical and theoretical bounds for the
Intrinsic Bayes Factor (IBF) across various models, including normal,
exponential, Poisson, geometric, linear, and ANOVA. We show that our new bounds
are useful, feasible, and change with the amount of information. We also
propose a methodology to construct the least favorable (for the null model)
intrinsic priors that result in the lower and upper bounds of the Intrinsic
Bayes Factors under certain conditions. Notably, our lower bounds exhibit
superior performance compared to the well-known -ep log(p) bound proposed by
Sellke et al. (2001) (Sellke et al., 2001) based on p-values.
