# Scalable computation for Bayesian hierarchical models

@inproceedings{Papaspiliopoulos2021ScalableCF, title={Scalable computation for Bayesian hierarchical models}, author={Omiros Papaspiliopoulos and Timoth'ee Stumpf-F'etizon and Giacomo Zanella}, year={2021} }

The article is about algorithms for learning Bayesian hierarchical models, the computational complexity of which scales linearly with the number of observations and the number of parameters in the model. It focuses on crossed random effect and nested multilevel models, which are used ubiquitously in applied sciences, and illustrates the methodology on two challenging real data analyses on predicting electoral results and real estate prices respectively. The posterior dependence in both classes…

## 2 Citations

### Exact Convergence Analysis for Metropolis-Hastings Independence Samplers in Wasserstein Distances

- Computer Science
- 2021

Under mild assumptions, it is shown the exact convergence rate in total variation is also exact in weaker Wasserstein distances for the Metropolis-Hastings independence sampler and can be upper bounded in Bayesian binary response regression when the sample size and dimension grow together.

### Convergence rate of a collapsed Gibbs sampler for crossed random effects models

- Mathematics, Computer Science
- 2021

The convergence rate of a collapsed Gibbs sampler for crossed random effects models is analyzed to apply to a substantially larger range of models than previous works, including models that incorporate missingness mechanism and unbalanced level data.

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