AI4BayesCode generates validated modular stateful MCMC samplers from natural language Bayesian model descriptions via LLM translation, modular blocks, and recursive stateful composition.
Hamiltonian Monte Carlo for Hierarchical Models
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abstract
Hierarchical modeling provides a framework for modeling the complex interactions typical of problems in applied statistics. By capturing these relationships, however, hierarchical models also introduce distinctive pathologies that quickly limit the efficiency of most common methods of in- ference. In this paper we explore the use of Hamiltonian Monte Carlo for hierarchical models and demonstrate how the algorithm can overcome those pathologies in practical applications.
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2026 1verdicts
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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.