Divide-and-Conquer: Towards Generalizable Amortized Bayesian Inference for the Drift Diffusion Model
The drift diffusion model (DDM) is a cornerstone of cognitive decision-making research. Although numerous estimation methods exist, researchers continue to seek inference approaches that are both fast and flexible across diverse study designs. Amortized Bayesian inference (ABI) can provide nearly instantaneous inference for complex stochastic models like the DDM, but neural networks trained for one study design cannot generalize to others. In this paper, we propose a divide-and-conquer framework that address this limitation. The core idea is that the DDM's independence assumption allows the full dataset to be decomposed into pairwise shards, each sharing a common structure that a single n...
| Authors | Yufei Wu, Shanqing Gao, Andreas Voss et al. |
|---|---|
| Primary category | stat.ML |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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