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| No-U-Turn Sampler (NUTS)× | Cadenes de Markov Monte Carlo (MCMC)× | |
|---|---|---|
| Camp | Bayesià | Bayesià |
| Família | Bayesian methods | Bayesian methods |
| Any d'origen≠ | 2014 | — |
| Autor original≠ | Matthew D. Hoffman & Andrew Gelman | — |
| Tipus≠ | Sampling algorithm (MCMC) | Posterior sampling algorithm |
| Font seminal≠ | Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(47), 1593–1623. link ↗ | 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 |
| Àlies≠ | NUTS, No-U-Turn HMC, adaptive Hamiltonian Monte Carlo, self-tuning HMC | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Relacionats≠ | 4 | 3 |
| Resum≠ | The No-U-Turn Sampler (NUTS) is a self-tuning Markov chain Monte Carlo algorithm introduced by Hoffman and Gelman (2014) that extends Hamiltonian Monte Carlo (HMC) by automatically determining the optimal number of leapfrog steps, eliminating the most sensitive manual tuning parameter. NUTS is the default sampler in Stan and PyMC and has made large-scale, high-dimensional Bayesian inference practically accessible without requiring users to set trajectory lengths by hand. | 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. |
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