I have run a series of Bayesian models with flat priors in which I obtain a posterior probability distribution for my coefficient of interest. The reviewer of my paper wishes us to classify these probability according to different strengths of evidences (strong, moderate, weak etc..). I have seen this done with Bayes factor, e.g., Lee and Wagenmakers' classification
I wish to avoid running the models with and without the coefficient. Instead, I thought I could compare the posterior and prior probability. However, I have uninformative priors so I think Bayes factor would not work(?). But I wonder whether I can still follow this classification, e.g., under the null prior, I expect 50% of the coefficient to be negative. While my posterior probability is 5%. From that, can I infer a Bayes factor of 10 (0.5/0.05), which would be strong evidence?
Or is there another classification scheme I could use?
Thanks for any help and especially any references?