Topic: Dirichlet Process

Unsupervised segmentation of behavior and neural activity into simpler alternating states is providing unprecedented insights onto the neural underpinnings of decision-making. Recurrent Switching Linear Dynamical Systems (rSLDS) models use observations or continuous latent activity to guide the switching in states, providing significant advantages in capturing important temporally and spatially sequential dependencies. In trying to allow for unbounded complexity in the discrete states, most approaches have focused on Dirichlet Process (DP) mixture models. Such non-parametric Bayesian models re