Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Gausa process× | Mārkova ķēžu Montekarlo (MCMC)× | |
|---|---|---|
| Nozare≠ | Mašīnmācīšanās | Bajesa metodes |
| Saime≠ | Machine learning | Bayesian methods |
| Izcelsmes gads≠ | 2006 (book); roots in Kriging, 1951) | — |
| Autors≠ | Rasmussen, C. E. & Williams, C. K. I. | — |
| Tips≠ | Probabilistic non-parametric model | Posterior sampling algorithm |
| Pirmavots≠ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 | 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≠ | GP, Gaussian Process Regression, GPR, Kriging | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks. | 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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