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| Logistische Regression× | Markov-Kette Monte Carlo (MCMC)× | |
|---|---|---|
| Fachgebiet≠ | Forschungsstatistik | Bayes-Statistik |
| Familie≠ | Process / pipeline | Bayesian methods |
| Entstehungsjahr≠ | 1958 | — |
| Urheber≠ | David Roxbee Cox | — |
| Typ≠ | Method | Posterior sampling algorithm |
| Wegweisende Quelle≠ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | 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 |
| Aliasnamen | logit model, binomial logistic regression, LR | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Verwandt | 3 | 3 |
| Zusammenfassung≠ | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. | 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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