EMHMM Simulation Study
This is an incremental study that addresses methodological accuracy for researchers using EMHMM in eye-tracking applications.
The authors investigated estimation errors in learning hidden Markov models (HMMs) for eye movement analysis using variational Bayesian inference, finding relationships between error metrics (KL divergence and L1-norm) and distortions in ground-truth parameters based on simulation studies with varying sequence numbers and lengths.
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.