Switching state-space modeling of neural signal dynamics
Fig 5
Simulation results: Segmentation performance on data generated from a different switching model class.
(a) Histograms of segmentation accuracy given true parameters across 200 repetitions. (b) Histograms of segmentation accuracy when model parameter were unknown across 200 repetitions. We followed the same strategy stated in Fig 3 when the true model parameters were not available. In both (a) and (b), the mean segmentation accuracy for each method is displayed and marked by the dashed red line. Random = random segmentation with a Bernoulli process; Static = static switching method; IMM = interacting multiple models method; VI-A = variational inference with deterministic annealing (orange color); VI-I = variational inference with interpolated densities (blue color). VI-A/VI-I EM denote the EM learning algorithms with the corresponding initialization procedure during E-steps.