mlsanityData to 7 Oct 2026

Hamiltonian Monte Carlo for Hierarchical Models

arXiv 1312.0906 ↗First posted 3 Dec 2013stat.MEcited by 50

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Abstract (arXiv)

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.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2013.

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Citing papers per year: 2020: 1; 2021: 6; 2022: 8; 2023: 7; 2024: 7; 2025: 9; 2026: 12.

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  2. 2024Cautious Optimizers: Improving Training with One Line of Codecited by 42
  3. 2021Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priorscited by 29
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  5. 2022Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlocited by 19
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  11. 2023Bayesian Hierarchical Models for Counterfactual Estimationcited by 6
  12. 2023On the hierarchical Bayesian modelling of frequency response functionscited by 6
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  6. 2025STeP: A Framework for Solving Scientific Video Inverse Problems with Spatiotemporal Diffusion Priorscited by 3
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  8. 2025Generalized Guarantees for Variational Inference in the Presence of Even and Elliptical Symmetrycited by 3
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  11. 2025The Within-Orbit Adaptive Leapfrog No-U-Turn Samplercited by 2
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  13. 2022Maximum entropy exploration in contextual bandits with neural networks and energy based modelscited by 1
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  34. 2026DTVEM-RE: A Hierarchical Random-Effects Extension of the Differential Time-Varying Effect Model for Person-Specific Multi-Lag Estimation in Intensive Longitudinal Datacited by 0
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  38. 2026Large Language Bayes Is Not Reparameterisation-Invariantcited by 0