Divide-and-Conquer: Towards Generalizable Amortized Bayesian Inference for the Drift Diffusion Model

This paper proposes a divide-and-conquer framework for amortized Bayesian inference in the drift diffusion model. Exploiting the model’s independence assumption, it decomposes a full dataset into pairwise shards with a shared structure, applies one neural inference network to each shard, and combines the resulting posteriors using consensus MCMC. According to the abstract, simulated experiments achieve accuracy and uncertainty comparable to conventional MCMC while reducing computational cost by several orders of magnitude. The approach targets a central limitation of amortized inference: poor transfer across study designs.