AI4BayesCode generates validated modular stateful MCMC samplers from natural language Bayesian model descriptions via LLM translation, modular blocks, and recursive stateful composition.
Krishnan and Payam Barnaghi , title =
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Proposes causal risk minimization via higher-order moment-balancing error decomposition and attribute projection for high-dimensional treatments, with experiments on continuous, discrete, and text data.
The paper reduces a broad set of prompt engineering techniques to six core approaches and applies them to life sciences use cases while addressing common LLM pitfalls.
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AI4BayesCode: From Natural Language Descriptions to Validated Modular Stateful Bayesian Samplers
AI4BayesCode generates validated modular stateful MCMC samplers from natural language Bayesian model descriptions via LLM translation, modular blocks, and recursive stateful composition.
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Causal Risk Minimization for High-Dimensional Treatments
Proposes causal risk minimization via higher-order moment-balancing error decomposition and attribute projection for high-dimensional treatments, with experiments on continuous, discrete, and text data.
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The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences
The paper reduces a broad set of prompt engineering techniques to six core approaches and applies them to life sciences use cases while addressing common LLM pitfalls.