IDyOM (Information Dynamics of Music) is a system for constructing multiple-viewpoint, variable-order Markov models (Conklin & Witten, 1995; Bunton, 1997) for predictive modelling of probabilistic structure in symbolic, sequential auditory domains such as music. IDyOM acquires knowledge about a domain through statistical learning and generates conditional probability distributions representing the estimated likelihood of each event in a sequence, plus associated information-theoretic measures, given the preceding context and prior short- and long-term training of the model. Scientific presentations of IDyOM can be found in Pearce (2005, 2018) - please use these as citations in any publications using this software.
Pearce, M. T. (2005). The Construction and Evaluation of Statistical Models of Melodic Structure in Music Perception and Composition. Doctoral Dissertation, Department of Computer Science, City University of London, UK.
Pearce, M. T. (2018). Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation. Annals of the New York Academy of Sciences, 1423, 378-395. https://doi.org/10.1111/nyas.13654