Amortized Bayesian Workflow
This addresses the computational bottleneck in Bayesian inference for researchers and practitioners dealing with large-scale datasets, though it appears incremental as it combines existing methods.
The paper tackles the trade-off between computational speed and sampling accuracy in Bayesian inference by proposing an adaptive workflow that integrates fast amortized inference with accurate MCMC techniques, achieving efficiency gains while maintaining high posterior quality on tens of thousands of datasets.
Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid amortized inference with gold-standard MCMC techniques to achieve a favorable combination of both speed and accuracy when performing inference on many observed datasets. Our approach uses principled diagnostics to guide the choice of inference method for each dataset, moving along the Pareto front from fast amortized sampling via generative neural networks to slower but guaranteed-accurate MCMC when needed. By reusing computations across steps, our workflow synergizes amortized and MCMC-based inference. We demonstrate the effectiveness of this integrated approach on several synthetic and real-world problems with tens of thousands of datasets, showing efficiency gains while maintaining high posterior quality.