  Eye Movement analysis with Hidden Markov Models (EMHMM) is a method for
modeling eye fixation sequences using hidden Markov models (HMMs). In this
report, we run a simulation study to investigate the estimation error for
learning HMMs with variational Bayesian inference, with respect to the number
of sequences and the sequence lengths. We also relate the estimation error
measured by KL divergence and L1-norm to a corresponding distortion in the
ground-truth HMM parameters.
