Extract Posterior-like Samples from Stored SGLD Trajectories

Build posterior-like samples from optimizer trajectories by dropping an initial burn-in segment and applying thinning.

Usage

ngme_sgld_samples(
  ngme,
  name = "all",
  burnin_iter = 0,
  thinning = 1,
  apply_transform = TRUE,
  combine_chains = TRUE
)

Arguments

ngme

fitted `ngme` object with `store_traj = TRUE`.

name

parameter block to extract: `"all"` (default), latent model name, or `"general"`.

burnin_iter

non-negative integer. Number of initial iterations to discard before sampling.

thinning

positive integer thinning interval.

apply_transform

logical; apply parameter transforms to user scale.

combine_chains

logical; if `TRUE`, return one combined data.frame, otherwise return one data.frame per chain.

Value

A data.frame (or list of data.frames when `combine_chains = FALSE`) with columns `.chain`, `.draw`, `.iter`, and one column per parameter.