Papers › Investigating the efficiency of marginalising over discrete parameters in Bayesian computations
Investigating the efficiency of marginalising over discrete parameters in Bayesian computations
Wen Zhang, Jeffrey Pullin, Lyle Gurrin, Damjan Vukcevic
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Bayesian analysis methods often use some form of iterative simulation such as Monte Carlo computation. Models that involve discrete variables can sometime pose a challenge, either because the methods used do not support such variables (e.g. Hamiltonian Monte Carlo) or because the presence of such variables can slow down the computation. A common workaround is to marginalise the discrete variables out of the model. While it is reasonable to expect that such marginalisation would also lead to more time-efficient computations, to our knowledge this has not been demonstrated beyond a few specialised models. We explored the impact of marginalisation on the computational efficiency for a few simple statistical models. Specifically, we considered two- and three-component Gaussian mixture models, and also the Dawid-Skene model for categorical ratings. We explored each with two software implementations of Markov chain Monte Carlo techniques: JAGS and Stan. We directly compared marginalised and non-marginalised versions of the same model using the samplers on the same software. Our results show that marginalisation on its own does not necessarily boost performance. Nevertheless, the best performance was usually achieved with Stan, which requires marginalisation. We conclude that there is no simple answer to whether or not marginalisation is helpful. It is not necessarily the case that, when turned 'on', this technique can be assured to provide computational benefit independent of other factors, nor is it likely to be the model component that has the largest impact on computational efficiency.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections