% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/batch-means.R
\name{ngme_sgld_samples}
\alias{ngme_sgld_samples}
\title{Extract Posterior-like Samples from Stored SGLD Trajectories}
\usage{
ngme_sgld_samples(
  ngme,
  name = "all",
  burnin_iter = 0,
  thinning = 1,
  apply_transform = TRUE,
  combine_chains = TRUE
)
}
\arguments{
\item{ngme}{fitted `ngme` object with `store_traj = TRUE`.}

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

\item{burnin_iter}{non-negative integer. Number of initial iterations to
discard before sampling.}

\item{thinning}{positive integer thinning interval.}

\item{apply_transform}{logical; apply parameter transforms to user scale.}

\item{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.
}
\description{
Build posterior-like samples from optimizer trajectories by dropping an
initial burn-in segment and applying thinning.
}
