Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal
Stochastic Linear Mixing Model
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal
Stochastic Linear Mixing Model
Many modern time-series datasets contain large numbers of output response variables sampled for prolonged periods of time. For example, in neuroscience, the activities of 100s-1000's of neurons are recorded during behaviors and in response to sensory stimuli. Multi-output Gaussian process models leverage the nonparametric nature of Gaussian processes to capture …