A commonly-cited reason for the poor performance of automatic chord estimation (ACE) systems within music information retrieval (MIR) is that non-chord tones (i.e., notes outside the supporting harmony) contribute to error during the labeling process. Despite the prevalence of machine-learning in MIR, there are cases where rule-based or statistical approaches provide a simpler alternative while allowing for insights into musicological practices. Conversely, deep-learning approaches are known as a “black box” where insights into the algorithm’s predictive processes are unavailable. In this talk, we present a statistical model for predicting chord tones. Our model is currently focused on predicting chord tones in classical music, since composition in this style is highly constrained, theoretically making the placement of chord tones highly predictable. Indeed, music theorists have labeling systems for every variety of non-chord tone, primarily classified by the note’s metric position and intervals of approach and departure. By using meter, duration, and melodic intervals as predictors, we build a theory-driven model for predicting chord tones on the complete TAVERN dataset. While our probabilistic approach is similar to other efforts in the domain of automatic harmonic analysis and ACE, our focus is on melodic reduction rather than predicting harmony. However, we are working towards applications for our model in assisting ACE. Our model is currently performing non-chord tone reduction with 73.5% accuracy across multiple datasets. We believe a chord-tone model could provide a reduction tool with many applications for computational and cognitive musicology as well as music theory pedagogy. In addition, we implement our melody-reduction model as a visualization program to assist melody reduction and non-chord tone identification for computational musicology researchers and music theorists.