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This paper discusses model-based rhythm and tempo analysis of music data in the MIDI format. The data is assumed to be obtained from a module performing multi-pitch analysis of music acoustic signals inside an automatic transcription system. In performed music, observed note lengths and local tempo fluctuate from the nominal note lengths and long-term tempo. Applying the framework of continuous speech recognition to rhythm recognition, we take a probabilistic top-down approach on the joint estimation of rhythm and tempo from the performed onset events in MIDI data. Short-term rhythm patterns are extracted from existing music samples and form a "rhythm vocabulary." Local tempo is represented by a smooth curve. The entire problem is formulated as an integrated optimization problem to maximize a posterior probability, which can be solved by an iterative algorithm which alternately estimates rhythm and tempo. Evaluation of the algorithm through various experiments is also presented.