Bayesian Spectral Analysis Models using Gaussian Process Priors
Description
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.