Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Slice Sampling× | Regresión bayesiana× | Cadenas de Markov Monte Carlo (MCMC)× | |
|---|---|---|---|
| Campo | Bayesiano | Bayesiano | Bayesiano |
| Familia | Bayesian methods | Bayesian methods | Bayesian methods |
| Año de origen≠ | 2003 | — | — |
| Autor original≠ | Radford M. Neal | — | — |
| Tipo≠ | MCMC sampling algorithm | Bayesian linear model | Posterior sampling algorithm |
| Fuente seminal≠ | Neal, R. M. (2003). Slice sampling (with discussion). Annals of Statistics, 31(3), 705–767. DOI ↗ | 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 | 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 |
| Alias≠ | slice sampler, Neal slice sampler, uniform slice sampling, auxiliary variable slice sampler | bayesian linear regression, probabilistic regression, bayesian regresyon | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Relacionados≠ | 4 | 2 | 3 |
| Resumen≠ | 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. | 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. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
| ScholarGateConjunto de datos ↗ |
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