">Vehtari, Gelman, and Gabry 2017</a>)</span>.</p>
<p>Partitioning the data and reusing the partitions is very fiddly in the indexes and may not lead to even divisions of the data. It’s far easier to use random partitions, which support arbitrarily sized test/training splits and can be easily implemented in Stan. The drawback is that the variance of the resulting estimate is higher than with a balanced block partition.</p>
<section id="stan-implementation-with-random-folds" class="level3 unnumbered">
<h3 