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Pooled Batch-Means Confidence Intervals from Multiple Chains

Source: R/batch-means.R
batch_means_ci.Rd

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.