• Corpus ID: 52983739

EMHMM Simulation Study

@article{Chan2018EMHMMSS,
  title={EMHMM Simulation Study},
  author={Antoni B. Chan and Janet Hui-wen Hsiao},
  journal={ArXiv},
  year={2018},
  volume={abs/1810.07435}
}
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. 
EMHMM: Eye Movement Analysis with Hidden Markov Models and Its Applications in Cognitive Research
TLDR
A novel eye movement data analysis method, Eye Movement analysis with Hidden Markov Models (EMHMM), which summarizes each individual’s eye movement pattern using a hidden Markov model (HMM; a type of machine learning model for time series data), including person-specific ROIs and transition probabilities among the ROIs.
Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM.
TLDR
A novel HMM-based clustering algorithm, the variational Bayesian hierarchical EM algorithm, which clusters HMMs through their densities and priors and simultaneously learns posteriors for the novel H MM cluster centers that compactly represent the structure of each cluster.

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Information Theory and Statistics