Porovnat metody

	Vícúrovňový Hamiltonovský Monte Carlo	Hierarchické Hamiltonovské Monte Carlo
Obor	Bayesovská statistika	Bayesovská statistika
Rodina	Bayesian methods	Bayesian methods
Rok vzniku	2010s	2015
Tvůrce	Beskos, Jasra, Law, Tempone, Zhou (multilevel MCMC); Neal (HMC component)	Betancourt & Girolami
Typ	Bayesian computational sampler	Bayesian sampling algorithm
Původní zdroj	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.	Betancourt, M. & Girolami, M. (2015). Hamiltonian Monte Carlo for hierarchical models. In S. K. Upadhyay, U. Singh, D. K. Dey & A. Loganathan (Eds.), Current Trends in Bayesian Methodology with Applications (pp. 79-101). CRC Press.
Další názvy	Multilevel HMC, MLHMC, multilevel HMC sampler, multilevel leapfrog MCMC	Hierarchical HMC, HMC for hierarchical models, HMC with reparameterization, NUTS for hierarchical Bayesian models
Příbuzné	5	5
Shrnutí	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.	Hierarchical Hamiltonian Monte Carlo (Hierarchical HMC) applies Hamiltonian Monte Carlo sampling to Bayesian hierarchical models, addressing the severe geometric challenges those models pose. By combining non-centered parameterizations with HMC's gradient-driven proposals, it achieves efficient posterior exploration of the multi-level funnel-shaped geometries that standard MCMC methods struggle with.