Sampling methods generate a finite set of network parameters whose empirical distribution approximates the posterior distribution as the number of samples increases. This can be achieved through techniques such as MC-dropout or by ensembling models. Ensembling can involve training with either randomly sampled data (bootstrapping) or the same data but different initial random weights (deep ensembling) . Although originally proposed as a non-Bayesian method, ensembling has been shown to offer similar advantages to Bayesian inference, and to provide more reliable uncertainty estimates than MC-dropout in practice .