Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Regresión bayesiana× | Hamiltonian Monte Carlo× | |
|---|---|---|
| Campo | Bayesiano | Bayesiano |
| Familia | Bayesian methods | Bayesian methods |
| Año de origen≠ | — | 1987 |
| Autor original | — | — |
| Tipo≠ | Bayesian linear model | Gradient-based Markov chain Monte Carlo sampler |
| Fuente 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 | Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters B, 195(2), 216–222. DOI ↗ |
| Alias≠ | bayesian linear regression, probabilistic regression, bayesian regresyon | HMC, Hybrid Monte Carlo, NUTS, No-U-Turn Sampler |
| Relacionados≠ | 2 | 3 |
| Resumen≠ | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. | 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. |
| ScholarGateConjunto de datos ↗ |
|
|