Computational Statistics ePosters

ePoster

Bayesian Inference in High-Dimensional Time-Series with the Orthogonal Stochastic Linear Mixing Model

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COSYNE 2022

The activity of 100s-1000’s of neurophysiological signals can be recorded during behaviors and in response to sensory stimuli, creating high-dimensional time-series data. Understanding such data is often aided by methods that extract low-dimensional latent structure present in the high-dimensional recordings.…

DOI 10.57736/ww-a7e1-73c3

high-dimensional time-series dataMulti-output Gaussian process modelsstochastic linear mixing model

Bayesian Inference in High-Dimensional Time-Series with the Orthogonal Stochastic Linear Mixing Model

Topics

Bayesian Inference in High-Dimensional Time-Series with the Orthogonal Stochastic Linear Mixing Model

Topics

Computational Statistics ePosters

Results

Bayesian Inference in High-Dimensional Time-Series with the Orthogonal Stochastic Linear Mixing Model

Computational Statistics ePosters

Results

Bayesian Inference in High-Dimensional Time-Series with the Orthogonal Stochastic Linear Mixing Model