bsamGP: Bayesian Spectral Analysis Models using Gaussian Process Priors
Contains functions to perform Bayesian inference
using a spectral analysis of Gaussian process priors.
Gaussian processes are represented with a Fourier series
based on cosine basis functions. Currently the package
includes parametric linear models, partial linear additive
models with/without shape restrictions, generalized linear
additive models with/without shape restrictions, and
density estimation model. To maximize computational
efficiency, the actual Markov chain Monte Carlo sampling
for each model is done using codes written in FORTRAN 90.
This software has been developed using funding supported by
Basic Science Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Education
(no. NRF-2016R1D1A1B03932178 and no. NRF-2017R1D1A3B03035235).
Please use the canonical form
to link to this page.