This paper examines the prediction and generation of music using a multiple viewpoint system, a collection of independent views of the musical surface each of which models a specific type of musical phenomena. Both the general style and a particular piece are modeled using dual short-term and long-term theories, and the model is created using machine learning techniques on a corpus of musical examples. The models are used for analysis and prediction, and we conjecture that highly predictive theories will also generate original, acceptable, works. Although the quality of the works generated is hard to quantify objectively, the predictive power of models can be measured by the notion of entropy, or unpredictability. Highly predictive theories will produce low-entropy estimates of a musical language. The methods developed are applied to the Bach chorale melodies. Multiple-viewpoint systems are learned from a sample of 95 chorales, estimates of entropy are produced, and a predictive theory is used to generate new, unseen pieces.