Optimal simulation-based Bayesian decisions

• URL: http://arxiv.org/abs/2311.05742v1
• Date: Thu, 9 Nov 2023 20:59:52 GMT
• Title: Optimal simulation-based Bayesian decisions
• Authors: Justin Alsing, Thomas D. P. Edwards, Benjamin Wandelt
• Abstract summary: We present a framework for the efficient computation of optimal Bayesian decisions under intractable likelihoods. We develop active learning schemes to choose where in parameter and action spaces to simulate. The resulting framework is extremely simulation efficient, typically requiring fewer model calls than the associated posterior inference task alone.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a framework for the efficient computation of optimal Bayesian decisions under intractable likelihoods, by learning a surrogate model for the expected utility (or its distribution) as a function of the action and data spaces. We leverage recent advances in simulation-based inference and Bayesian optimization to develop active learning schemes to choose where in parameter and action spaces to simulate. This allows us to learn the optimal action in as few simulations as possible. The resulting framework is extremely simulation efficient, typically requiring fewer model calls than the associated posterior inference task alone, and a factor of $100-1000$ more efficient than Monte-Carlo based methods. Our framework opens up new capabilities for performing Bayesian decision making, particularly in the previously challenging regime where likelihoods are intractable, and simulations expensive.

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