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The performance of a model is dependent not only on the amount of knowledge available to the model but also on how the knowledge is exploited. We investigate the recognition of handwritten musical notation based on three related probabilistic inference techniques: Hidden Markov Models (HMMs), Markov Models (MMs) and Naïve Bayes (NBs). Music notes are written on a tablet. A sequence of ink patterns representing this symbol is captured and subsequently employed for constructing the models of HMMs, MMs and NBs. The proposed approach exploits both global and local information derived from ink patterns which we have demonstrated the exploitation of this information via different features employed in different HMMs. The specificity and sensitivity measures of these classification models are compared using unseen test datasets. The findings show that HMM outperformed MM and NB models, due to the ability of HMM in exploiting both transitional probability (transition matrix A) and the overall likelihood of the observed events (emission matrix B). Also, HMMs with more hidden states outperformed those with less states, since a larger model has more capacity. In conclusion, our approach demonstrated that HMM can better exploit information extracted from ink patterns than models of MM or NB, and therefore is an optimal inference technique to encoding useful information for musical notation representation.