title: IterativeImputer - sample_posterior
body: <p>Sklearn implements an imputer called the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html" rel="nofollow noreferrer">IterativeImputer</a>. I believe that it works by predicting the values for missing features values in a round robin fashion, using an estimator.</p>
  <p>It has an argument called sample_posterior but I can't seem to figure out when I should use it.</p>
  <blockquote>
  <p>sample_posterior boolean, default=False</p>
  <p>Whether to sample from the (Gaussian) predictive posterior of the fitted estimator for each imputation. Estimator must support
  return_std in its predict method if set to True. Set to True if using
  IterativeImputer for multiple imputations.</p>
  </blockquote>
  <p>I looked at the source code but it still wasn't clear to me. Should I use this if I have multiple features that I am going to fill using the iterative imputer or should I use this if I plan to use the imputer multiple times like for a training and then validation set?</p>