Abstract
In this paper, several distance measures for hidden Markov models (HMMs) are compared. The most commonly used distance measure between two HMMs is Kullback-Leibler divergence (KLD). Since there is no closed form solution, Monte-Carlo method is usually applied to calculate the KLD. However, the computational complexity in Monte-Carlo estimation may be prohibitive in practical applications, which motivated researchers to propose new distance measures for HMMs. Numerical examples are presented comparing three such distance measures against the Monte-Carlo method. Results show that it is possible to approximate the KLD with a saving of hundreds of times in computational complexity
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