GP: Specify dynamic Gaussian process trends in 'mvgam' models

In nicholasjclark/mvgam: Multivariate (Dynamic) Generalized Additive Models

Specify dynamic Gaussian process trends in mvgam models

Description

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.

Usage

GP(...)

Arguments

...

unused

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.

Value

An object of class mvgam_trend, which contains a list of arguments to be interpreted by the parsing functions in mvgam

Author(s)

Nicholas J Clark

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

See Also

gp

nicholasjclark/mvgam documentation built on April 17, 2025, 9:39 p.m.