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. Author manuscript; available in PMC: 2018 Jul 15.
Published in final edited form as: Neuroimage. 2017 Mar 4;155:271–290. doi: 10.1016/j.neuroimage.2017.02.083

Figure 7.

Figure 7

Performance of BSFA versus Gaussian-HMM models with respect to observation noise. Data were generated as described in § 3.1.4. The noise variance in data generation was varied from 0.01 to 1000. Experiment was repeated for 10 runs with random initializations. Each run is shown in a unique color. The results are plotted as “·” symbols with small horizontal perturbations for better visualization. (A) Estimated number of states in each run and for each condition given by Viterbi algorithm and computed using BSFA. (B) Pearson correlation between true covariance and estimated covariance matrices averaged across all states and all runs computed from BSFA. (C) Estimated number of states in each run and for each condition given by Viterbi algorithm and computed using Gaussian-HMM model. (D) Pearson correlation between true and estimated covariance matrices computed from Gaussian-HMM model.