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| Metropolis-Hastings Multillivell× | Hamiltonian Monte Carlo Multillivell× | |
|---|---|---|
| Camp | Bayesià | Bayesià |
| Família | Bayesian methods | Bayesian methods |
| Any d'origen≠ | 1953 (core); 1990s (multilevel application) | 2010s |
| Autor original≠ | Metropolis et al. (1953); hierarchical extension developed through 1980s–1990s Bayesian computation literature | Beskos, Jasra, Law, Tempone, Zhou (multilevel MCMC); Neal (HMC component) |
| Tipus≠ | MCMC sampling algorithm | Bayesian computational sampler |
| Font seminal≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Beskos, A., Jasra, A., Law, K., Tempone, R., & Zhou, Y. (2017). Multilevel sequential Monte Carlo samplers. Stochastic Processes and their Applications, 127(5), 1417–1440. DOI ↗ |
| Àlies | hierarchical Metropolis-Hastings, multilevel MH, MH for hierarchical models, blocked Metropolis-Hastings | Multilevel HMC, MLHMC, multilevel HMC sampler, multilevel leapfrog MCMC |
| Relacionats≠ | 6 | 5 |
| Resum≠ | Multilevel Metropolis-Hastings applies the Metropolis-Hastings MCMC algorithm to hierarchical (multilevel) Bayesian models, sampling jointly from group-level parameters and hyperparameters by proposing candidate values and accepting or rejecting them via a ratio that respects the full joint posterior across all levels of the model. | Multilevel Hamiltonian Monte Carlo (Multilevel HMC) combines the variance-reduction strategy of multilevel Monte Carlo with the efficient gradient-driven exploration of Hamiltonian Monte Carlo. By running coupled HMC chains at increasing levels of model fidelity or discretisation, it achieves accurate posterior estimates at a computational cost substantially lower than a single fine-level HMC chain. |
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