Comparar métodos

	Slice Sampling	Monte Carlo Hamiltoniano
Área	Bayesiano	Bayesiano
Família	Bayesian methods	Bayesian methods
Ano de origem	2003	1987
Autor original	Radford M. Neal	—
Tipo	MCMC sampling algorithm	Gradient-based Markov chain Monte Carlo sampler
Fonte seminal	Neal, R. M. (2003). Slice sampling (with discussion). Annals of Statistics, 31(3), 705–767.	Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters B, 195(2), 216–222.
Outros nomes	slice sampler, Neal slice sampler, uniform slice sampling, auxiliary variable slice sampler	HMC, Hybrid Monte Carlo, NUTS, No-U-Turn Sampler
Relacionados	4	3
Resumo	Slice sampling is a Markov chain Monte Carlo (MCMC) algorithm introduced by Radford M. Neal in his 2003 Annals of Statistics paper. It generates samples from a target distribution by drawing uniformly from the region under the density curve — called the 'slice' — without requiring the user to specify a step-size or proposal distribution, making it self-tuning and broadly applicable for Bayesian posterior inference.	Hamiltonian Monte Carlo (HMC) is a gradient-based Markov chain Monte Carlo algorithm that uses the geometry of the log-posterior surface to make large, informed jumps through parameter space instead of the small random steps of classical MCMC. Originally introduced for lattice field theory by Duane, Kennedy, Pendleton, and Roweth (1987) under the name Hybrid Monte Carlo, and brought into mainstream statistics by Radford Neal's authoritative 2011 chapter, HMC is today the default sampler in Stan and PyMC and is widely regarded as the state-of-the-art engine for Bayesian posterior inference in high-dimensional models.