发表机构
KU Leuven; Heidelberg University（鲁汶大学; 海德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对摊销贝叶斯推断无法推广至不同研究设计的局限，提出分而治之框架，将DDM数据集分解为成对数据片后单独推断再用共识MCMC结合，在保持与MCMC相当性能的同时大幅降低计算成本，为ABI方法提供通用优化策略。
AI 中文摘要
漂移扩散模型（DDM）是认知决策研究的基石。尽管存在大量估计方法，研究人员仍在寻找对不同研究设计兼具快速性与灵活性的推断方法。摊销贝叶斯推断（ABI）可为DDM这类复杂随机模型提供近乎瞬时的推断，但针对一种研究设计训练的神经网络无法推广至其他设计。本文提出一种分而治之框架以解决这一局限，核心思路是DDM的独立性假设允许将完整数据集分解为成对数据片，每个数据片具有单一神经网络可学习的共同结构。对每个数据片单独执行推断，所得后验通过共识MCMC结合以近似完整后验。使用模拟数据集评估该方法的准确性与不确定性，结果表明，所提分而治之方法的准确性与不确定性可与MCMC相当，同时计算成本降低数个数量级。本研究不仅推进了DDM估计，还展示了一种提升ABI方法在不同应用中的可扩展性与可推广性的通用策略。
英文摘要
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 neural network can learn. Inference is performed on each shard separately and the resulting posteriors are combined via consensus MCMC to approximate the full posterior. Using simulated datasets, we evaluate the accuracy and uncertainty of this method. Our results show that the proposed divide-and-conquer approach achieves accuracy and uncertainty comparable to MCMC while reducing computational cost by several orders of magnitude. This work not only advances DDM estimation but also demonstrates a general strategy for improving the scalability and generalizability of ABI methods across diverse applications.
