Switching state-space modeling of neural signal dynamics
Fig 2
Simulation results: Segmentation performance when true parameters are known.
(a) An example simulated sequence switching between two AR1 models with different dynamics. The top panel shows the time trace with the two states marked in different colors. The bottom panel shows switching inference results on this example given true parameters of the underlying generative model. Time points estimated to be in the first model (st = 1) are marked in colored dots for each inference method, with accuracy shown in parentheses. (b) Histograms of segmentation accuracy across 200 repetitions. The mean segmentation accuracy for each method is displayed and marked by the dashed red line. True = ground truth; 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).