= Gaussian-process factor analysis
{title2=$y_t=Cz_t+b+\varepsilon_t$}
= GPFA
{c}
{synonym}
A model combining a linear observation equation with <Gaussian process> priors over latent trajectories, allowing shared neural activity and temporal smoothness to be estimated together. Observation-noise assumptions and temporal kernels are substantive choices. The original 2009 demonstration used macaque motor recordings, not episodic-memory recordings.<Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity by Byron M. Yu and others>[2009, model formulation and motor-cortex demonstration]{ref}
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