ess_bulk  R Documentation 
Compute a bulk effective sample size estimate (bulkESS) for a single
variable. BulkESS is useful as a diagnostic for the sampling efficiency in
the bulk of the posterior. It is defined as the effective sample size for
rank normalized values using split chains. For the tail effective sample size
see ess_tail()
. See Vehtari (2021) for an indepth
comparison of different effective sample size estimators.
ess_bulk(x, ...)
## Default S3 method:
ess_bulk(x, ...)
## S3 method for class 'rvar'
ess_bulk(x, ...)
x 
(multiple options) One of:

... 
Arguments passed to individual methods (if applicable). 
If the input is an array, returns a single numeric value. If any of the draws
is nonfinite, that is, NA
, NaN
, Inf
, or Inf
, the returned output
will be (numeric) NA
. Also, if all draws within any of the chains of a
variable are the same (constant), the returned output will be (numeric) NA
as well. The reason for the latter is that, for constant draws, we cannot
distinguish between variables that are supposed to be constant (e.g., a
diagonal element of a correlation matrix is always 1) or variables that just
happened to be constant because of a failure of convergence or other problems
in the sampling process.
If the input is an rvar
, returns an array of the same dimensions as the
rvar
, where each element is equal to the value that would be returned by
passing the draws array for that element of the rvar
to this function.
Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and PaulChristian Bürkner (2021). Ranknormalization, folding, and localization: An improved Rhat for assessing convergence of MCMC (with discussion). Bayesian Data Analysis. 16(2), 667–718. doi:10.1214/20BA1221
Aki Vehtari (2021). Comparison of MCMC effective sample size estimators. Retrieved from https://avehtari.github.io/rhat_ess/ess_comparison.html
Other diagnostics:
ess_basic()
,
ess_quantile()
,
ess_sd()
,
ess_tail()
,
mcse_mean()
,
mcse_quantile()
,
mcse_sd()
,
pareto_diags()
,
pareto_khat()
,
rhat()
,
rhat_basic()
,
rhat_nested()
,
rstar()
mu < extract_variable_matrix(example_draws(), "mu")
ess_bulk(mu)
d < as_draws_rvars(example_draws("multi_normal"))
ess_bulk(d$Sigma)
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