sampleBTF: Sampler for first or second order random walk (RW) Gaussian...

In dsp: Dynamic Shrinkage Process and Change Point Detection

Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM)

Description

Compute one draw of the T x 1 state variable mu in a DLM using back-band substitution methods. This model is equivalent to the Bayesian trend filtering (BTF) model, assuming appropriate (shrinkage/sparsity) priors for the evolution errors.

Usage

sampleBTF(
  y,
  obs_sigma_t2,
  evol_sigma_t2,
  D = 1,
  loc_obs = NULL,
  chol0 = NULL,
  prior_mean = NULL
)

Arguments

y	the T x 1 vector of time series observations
obs_sigma_t2	the T x 1 vector of observation error variances
evol_sigma_t2	the T x 1 vector of evolution error variances
D	the degree of differencing (one or two)
loc_obs	list of the row and column indices to fill in a band-sparse matrix
chol0	(optional) the m x m matrix of initial Cholesky factorization; if NULL, use the Matrix package for sampling, otherwise use the spam package
prior_mean	optional (default is NULL); numeric T x 1 vector specifying the prior mean of mu

Value

T x 1 vector of simulated states

Note

Missing entries (NAs) are not permitted in y. Imputation schemes are available.

dsp documentation built on Aug. 21, 2025, 5:29 p.m.