  Bayesian inference allows machine learning models to express uncertainty.
Current machine learning models use only a single learnable parameter
combination when making predictions, and as a result are highly overconfident
when their predictions are wrong. To use more learnable parameter combinations
efficiently, these samples must be drawn from the posterior distribution.
Unfortunately computing the posterior directly is infeasible, so often
researchers approximate it with a well known distribution such as a Gaussian.
In this paper, we show that through the use of high-capacity persistent
storage, models whose posterior distribution was too big to approximate are now
feasible, leading to improved predictions in downstream tasks.
