JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference

• URL: http://arxiv.org/abs/2512.22999v1
• Date: Sun, 28 Dec 2025 16:54:43 GMT
• Title: JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
• Authors: Niels Bracher, Lars Kühmichel, Desi R. Ivanova, Xavier Intes, Paul-Christian Bürkner, Stefan T. Radev,
• Abstract summary: JADAI is a framework that jointly amortizes Bayesian adaptive design and inference.<n>It achieves superior or competitive performance across standard adaptive design benchmarks.
• Score: 6.93922383439314
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain. To this end, we introduce JADAI, a framework that jointly amortizes Bayesian adaptive design and inference by training a policy, a history network, and an inference network end-to-end. The networks minimize a generic loss that aggregates incremental reductions in posterior error along experimental sequences. Inference networks are instantiated with diffusion-based posterior estimators that can approximate high-dimensional and multimodal posteriors at every experimental step. Across standard adaptive design benchmarks, JADAI achieves superior or competitive performance.

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