q-Analogue of Hamiltonian Monte Carlo method
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917301650980864 |
|---|---|
| author | Yang, Xiaomei Deng, Zhiliang |
| author_facet | Yang, Xiaomei Deng, Zhiliang |
| contents | Building upon Lagrangian mechanics on Wess's $q$-commutative spaces, we derive the $q$-deformed Hamiltonian dynamics as formulated by Lavagno et al. (2006). We then develop a computationally tractable scheme and propose a novel Hamiltonian Monte Carlo sampler ($q$-HMC). The proposed $q$-HMC method is shown to satisfy the detailed balance principle. Numerical experiments on distributions with explicit potential functions demonstrate its efficacy, particularly in exploring stiff energy landscapes. This method is also applied to draw samples from the Bayesian posterior distribution of inverse problems. The numerical test for the posterior distribution with stiff potential further shows the advantage of $q$-HMC. And it yields the identical computational implementation process to that of HMC when used to deal with functional reconstruction problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13246 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | q-Analogue of Hamiltonian Monte Carlo method Yang, Xiaomei Deng, Zhiliang Numerical Analysis Statistics Theory Building upon Lagrangian mechanics on Wess's $q$-commutative spaces, we derive the $q$-deformed Hamiltonian dynamics as formulated by Lavagno et al. (2006). We then develop a computationally tractable scheme and propose a novel Hamiltonian Monte Carlo sampler ($q$-HMC). The proposed $q$-HMC method is shown to satisfy the detailed balance principle. Numerical experiments on distributions with explicit potential functions demonstrate its efficacy, particularly in exploring stiff energy landscapes. This method is also applied to draw samples from the Bayesian posterior distribution of inverse problems. The numerical test for the posterior distribution with stiff potential further shows the advantage of $q$-HMC. And it yields the identical computational implementation process to that of HMC when used to deal with functional reconstruction problems. |
| title | q-Analogue of Hamiltonian Monte Carlo method |
| topic | Numerical Analysis Statistics Theory |
| url | https://arxiv.org/abs/2512.13246 |