Risk is the probability of an adverse event or outcome. In a previous article, I compared the Bayesian and Frequentist models of defining probability. This article compares the Bayesian and regression models of quantifying probability. Both approaches are widely used in the biomedical and behavioral sciences even though they yield different results. No consensus has emerged as to which is more appropriate. The choice between them remains controversial. This article concludes that the Bayesian model provides a viable alternative to logistic regression and may be more useful in quantifying the absolute recidivism risk of individual sex offenders. It shows how evaluators can easily calculate Bayesian probabilities and their associated credible intervals from an actuarial data set. Last, the article proposes a forensic practice guideline that evaluators do not conclude that an offender meets an absolute risk threshold unless the subject’s risk exceeds the threshold by a credible margin of error.