27min chapter

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#117 Unveiling the Power of Bayesian Experimental Design, with Desi Ivanova

Learning Bayesian Statistics

CHAPTER

Navigating Computational Challenges in Bayesian Experimental Design

This chapter explores the complexities and computational challenges in Bayesian experimental design (BED), focusing on the balance between resource allocation and Bayesian optimality. It highlights advancements in amortized Bayesian inference and the integration of simulation-based methods to streamline posterior inference. The discussion emphasizes practical implications for researchers, particularly in selecting appropriate models and designing experiments that adapt to real-time data.

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