방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 베이지안 포아송 회귀× | 베이지안 음이항 회귀× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1989 (GLM foundation); Bayesian treatment formalized in 1990s–2000s | 1990s–2000s |
| 창시자≠ | Gelman et al. (BDA); classical Poisson GLM from McCullagh & Nelder (1989) | Gelman, Carlin, Stern, Dunson, Vehtari & Rubin; Cameron & Trivedi |
| 유형≠ | Bayesian generalized linear model for count data | Bayesian GLM for overdispersed counts |
| 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 NB regression, Bayesian negbin model, Bayesian overdispersed count regression, Bayesian NB-2 model |
| 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. | Bayesian Negative Binomial Regression models non-negative integer count outcomes that exhibit overdispersion — where the variance exceeds the mean — by placing a negative binomial likelihood on the data and specifying prior distributions over the regression coefficients and the dispersion parameter. Posterior inference is typically performed via Markov chain Monte Carlo (MCMC) or variational methods, yielding full posterior distributions rather than point estimates. |
| ScholarGate데이터셋 ↗ |
|
|