batch_means_ci: Pooled Batch-Means Confidence Intervals from Multiple Chains

In ngme2: Linear Latent Non-Gaussian Models with Flexible Distributions

Pooled Batch-Means Confidence Intervals from Multiple Chains

Description

Compute pooled point estimates, covariance, and Wald confidence intervals from multiple chain trajectories.

Usage

batch_means_ci(
  chain_trajectories,
  level = 0.95,
  alpha = 0.501,
  M = NULL,
  N = NULL,
  drop_burnin = TRUE,
  burnin_iter = 0
)

Arguments

chain_trajectories	list of numeric matrices, each with rows = iterations and columns = parameters.
level	confidence level.
alpha	stepsize decay exponent in (1/2, 1).
M	number of retained batches per chain (excluding burn-in batch 0).
N	decorrelation constant used in the batch boundary formula.
drop_burnin	logical; if 'TRUE', discard batch 0.
burnin_iter	non-negative integer. Explicitly discard the first 'burnin_iter' iterations of each chain before Xi-style batching.

Value

A list with pooled estimates, covariance, standard errors, and confidence intervals.

ngme2 documentation built on May 20, 2026, 9:10 a.m.