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| No-U-Turn Sampler (NUTS)× | Beijesiskā regresija× | |
|---|---|---|
| Nozare | Bajesa metodes | Bajesa metodes |
| Saime | Bayesian methods | Bayesian methods |
| Izcelsmes gads≠ | 2014 | — |
| Autors≠ | Matthew D. Hoffman & Andrew Gelman | — |
| Tips≠ | Sampling algorithm (MCMC) | Bayesian linear model |
| Pirmavots≠ | 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 |
| Citi nosaukumi≠ | NUTS, No-U-Turn HMC, adaptive Hamiltonian Monte Carlo, self-tuning HMC | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Saistītās≠ | 4 | 2 |
| Kopsavilkums≠ | 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. | 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. |
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