Bayesian Nonparametric Spectral Density Estimation Using B-Spline Priors
Bayesian Nonparametric Spectral Density Estimation Using B-Spline Prio...
Generate a B-spline density basis of any degree
C++ function for building a density mixture, given mixture weights and...
FFT: Compute F_n X_n with the real-valued Fourier matrix F_n
Metropolis-within-Gibbs sampler for spectral inference of a stationary...
log Whittle likelihood
Help function: Fuller Logarithm
Unnormalised log posterior
Unnormalised log joint prior
C++ function for computing mixture weights of Bernstein-Mixtures given...
C++ function for generating p from v in Stick Breaking DP representati...
Plot method for psd class
Analytical spectral density for mean-centred ARMA(p,q) model
Compute unnormalised PSD using random mixture of B-splines
Help function: Uniform maximum
C++ help function to redundantly roll out a PSD to length n
Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series. The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented in Edwards, M.C., Meyer, R. and Christensen, N., Statistics and Computing (2018). <doi.org/10.1007/s11222-017-9796-9>.