The premise of approximate MCMC in Bayesian deep learning. (arXiv:2208.11389v1 [stat.ML])

This paper identifies several characteristics of approximate MCMC in Bayesian
deep learning. It proposes an approximate sampling algorithm for neural
networks. By analogy to sampling data batches from big datasets, it is proposed
to sample parameter subgroups from neural network parameter spaces of high
dimensions. While the advantages of minibatch MCMC have been discussed in the
literature, blocked Gibbs sampling has received less research attention in
Bayesian deep learning.

arxiv bayesian bayesian deep learning deep learning learning mcmc ml