Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовская регрессия× | DAG Causal Identification× | Метод Монте-Карло по цепям Маркова (MCMC)× | |
|---|---|---|---|
| Область≠ | Байесовские методы | Причинно-следственный вывод | Байесовские методы |
| Семейство≠ | Bayesian methods | Regression model | Bayesian methods |
| Год появления≠ | — | 2009 | — |
| Автор метода≠ | — | Judea Pearl | — |
| Тип≠ | Bayesian linear model | Causal identification framework | Posterior sampling algorithm |
| Основополагающий источник≠ | 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 | Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606 | 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 |
| Другие названия≠ | bayesian linear regression, probabilistic regression, bayesian regresyon | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Связанные≠ | 2 | 5 | 3 |
| Сводка≠ | 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. | DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths. | 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. |
| ScholarGateНабор данных ↗ |
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