Set up low-rank approximate Gaussian Process trend models using Hilbert basis expansions in mvgam. This function does not evaluate its arguments – it exists purely to help set up a model with particular GP trend models.
Value
An object of class mvgam_trend, which contains a list of
arguments to be interpreted by the parsing functions in mvgam.
Details
A GP trend is estimated for each series using Hilbert space
approximate Gaussian Processes. In mvgam, latent squared exponential GP
trends are approximated using by default 20 basis functions and
using a multiplicative factor of c = 5/4, which saves computational
costs compared to fitting full GPs while adequately estimating GP
alpha and rho parameters.
References
Riutort-Mayol G, Burkner PC, Andersen MR, Solin A and Vehtari A (2023). Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming. Statistics and Computing 33, 1. https://doi.org/10.1007/s11222-022-10167-2