Amortized Bayesian experimental design with sequential Monte Carlo

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• Sahel Mohammad Iqbal (Aalto University)
• Tuesday 24 June 2025, 10:15-10:45
• Seminar Room 1, Newton Institute.

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RCLW03 - Accelerating statistical inference and experimental design with machine learning

Most existing works on amortized Bayesian experimental design (BED) rely on contrastive estimators of the expected information gain (EIG). In this talk, I introduce a new framework for BED grounded in the control-as-inference paradigm. The task of collecting informative trajectories is reframed as a sampling problem from a non-Markovian state-space model. To address the resulting inference challenges, I present a nested sequential Monte Carlo algorithm tailored to this setting. This approach offers a fresh perspective on BED , and we end with ideas for improving the scalability of this algorithm.

This talk is part of the Isaac Newton Institute Seminar Series series.

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