Pseudo Bayes probability estimates are weighted averages of raw and modeled probabilities; these estimates have been studied primarily in nonpsychometric contexts. The purpose of this study was to evaluate pseudo Bayes probability estimates as applied to the estimation of psychometric test score distributions and chained equipercentile equating functions. Population test score distributions were created from actual test data and random samples of varied size were drawn from the populations. Pseudo Bayes estimation was applied to the random samples, using ranges of loglinear models and weights in the pseudo Bayes estimates' weighted averages of the raw and modeled test score probabilities. Equipercentile equating functions based on the pseudo Bayes estimates were also evaluated. Results indicated that the pseudo Bayes estimates have the potential to improve estimation accuracy for test score distributions and chained equipercentile equating functions in situations where loglinear modeling is not ideal and where finding the population loglinear model selection is not likely.