Abstract

My focus in this paper is on how the basic Bayesian model can be amended to reflect the role of idealizations and approximations in the confirmation or disconfirmation of any hypothesis. I suggest the following as a plausible way of incorporating idealizations and approximations into the Bayesian condition for incremental confirmation: Theory T is confirmed by observation P relative to background knowledge B iff Pr&B) > PrandB), where I is the conjunction of idealizations and approximations used in deriving the prediction PT from T, P