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| Catena di Markov Monte Carlo (MCMC)× | Regression with Ordinary Least Squares (OLS)× | |
|---|---|---|
| Campo≠ | Bayesiano | Econometria |
| Famiglia≠ | Bayesian methods | Regression model |
| Anno di origine≠ | — | 2019 |
| Ideatore≠ | — | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Posterior sampling algorithm | Linear regression |
| Fonte seminale≠ | 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 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Correlati≠ | 3 | 5 |
| Sintesi≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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