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| 베이지안 포아송 회귀× | 베이즈 일반화 선형 모형× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1989 (GLM foundation); Bayesian treatment formalized in 1990s–2000s | 1989 (GLM); 1995 (Bayesian BDA) |
| 창시자≠ | Gelman et al. (BDA); classical Poisson GLM from McCullagh & Nelder (1989) | McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al. |
| 유형≠ | Bayesian generalized linear model for count data | Bayesian regression model |
| 원전 | 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 | 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 |
| 별칭 | Bayesian log-linear count model, Bayesian GLM Poisson, Poisson regression with priors, Bayesian count regression | Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM |
| 관련 | 6 | 6 |
| 요약≠ | Bayesian Poisson regression models non-negative integer count outcomes using a Poisson likelihood with a log link, placing prior distributions on the regression coefficients. Posterior inference — combining prior beliefs with the data likelihood — produces full probability distributions over the coefficients rather than single-point estimates, enabling coherent uncertainty quantification and incorporation of domain knowledge. | A Bayesian Generalized Linear Model (Bayesian GLM) extends the classical GLM framework by placing prior distributions on the regression coefficients and updating them with data via Bayes' theorem. This yields a full posterior distribution over parameters rather than single point estimates, enabling richer uncertainty quantification and principled incorporation of prior knowledge for any exponential-family outcome. |
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