Researchers compare data assimilation and likelihood-based inference for agent-based models

A new paper systematically compares Data Assimilation (DA) and Likelihood-Based Inference (LBI) for estimating latent states in Agent-Based Models (ABMs). While DA is broadly applicable and good for aggregate predictions, LBI offers more precise agent-level inference by directly using the model's likelihood function. The study found LBI superior for individual-level forecasts, even with model mis-specification, whereas DA remains competitive for aggregate outcomes. AI

排序理由 Academic paper comparing two inference methods on a specific model type.

在 arXiv cs.LG 阅读 →

• arXiv
• Data Assimilation
• Likelihood-Based Inference

• 论文
• 其他

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →