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

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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

RANK_REASON Academic paper comparing two inference methods on a specific model type.

Read on arXiv cs.LG →

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

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Academic paper comparing two inference methods on a specific model type.
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Blas Kolic, Corrado Monti, Gianmarco De Francisci Morales, Marco Pangallo ·

    Comparing Data Assimilation and Likelihood-Based Inference on Latent State Estimation in Agent-Based Models

    arXiv:2509.17625v2 Announce Type: replace Abstract: In this paper, we present the first systematic comparison of Data Assimilation (DA) and Likelihood-Based Inference (LBI) in the context of an Agent-Based Model (ABM). These models generate observable time series driven by evolvi…

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