Module Contents


ess(→ List[int])

Compute the Effective Sample Size (ESS) of a sample

uwerr(→ List[int])

TransportMaps.Samplers.Routines.ess(samps, quantile=0.99, do_xcorr=False, plotting=False, plot_lag=50, fig=None) List[int][source]

Compute the Effective Sample Size (ESS) of a sample

The minimum ESS over all the dimension is returned. Cross-correlation can be optionally used as well in the determination of the ESS. Plotting of the correlation decay can be shown.

The ESS is computed as \(\lfloor m/\kappa \rfloor\), where

\[\kappa = 1 + \sum_{c_i>b_i} c_i \;,\]

\(c_i\) is the auto-correlation at lag \(i\) and \(b_i\) is the quantile-confidence interval for the \(i\)-th value of auto-correlation (i.e. only significant auto-correlation values are summed up).

  • samps (ndarray [\(m,d\)]) – \(d\)-dimensional sample on which to compute the ESS

  • quantile (float) – condifence interval quantile

  • do_xcorr (bool) – whether to compute and use the auto-correlation function

  • plotting (bool) – whether to plot auto/cross-correlation decays

  • plot_lag (int) – how many lags to plot

  • fig (figure) – handle to a figure


(int) – minimum ESS across the \(d\) dimensions

TransportMaps.Samplers.Routines.uwerr(samps, plotting=False) List[int][source]