In small-budget RCTs where significance tests decide scale-up, optimal pilot sampling shifts from representative to single homogeneous subpopulation as budget shrinks.
Modern Bayesian Experimental Design
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CA-BED uses Bayesian experimental design and simulated conversation trees with LLM likelihoods to optimize multi-turn question selection, reporting 21.8% higher success rates than direct prompting on entity-deduction benchmarks.
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
citing papers explorer
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When Representative Samples Produce Worse Outcomes: Scale-up Decisions and Testing in Small-Budget RCTs
In small-budget RCTs where significance tests decide scale-up, optimal pilot sampling shifts from representative to single homogeneous subpopulation as budget shrinks.
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CA-BED: Conversation-Aware Bayesian Experimental Design
CA-BED uses Bayesian experimental design and simulated conversation trees with LLM likelihoods to optimize multi-turn question selection, reporting 21.8% higher success rates than direct prompting on entity-deduction benchmarks.
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Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.