Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Regresi Logistik Multinomial Bayesian× | Regresi Logistik Ordinal× | |
|---|---|---|
| Bidang | Statistika | Statistika |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 1966 (classical); Bayesian extensions established by 1990s | 1980 |
| Pencetus≠ | Gelman et al. (Bayesian treatment); classical multinomial logit by Cox (1966) | Peter McCullagh |
| Tipe≠ | Bayesian classification model | Ordinal regression / GLM |
| Sumber perintis≠ | 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 | McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142. DOI ↗ |
| Alias | Bayesian polytomous logistic regression, Bayesian multinomial logit, Bayesian softmax regression, Bayesian nominal logistic regression | proportional-odds model, cumulative link model, ordered logit, OLR |
| Terkait≠ | 5 | 6 |
| Ringkasan≠ | Bayesian Multinomial Logistic Regression models a nominal outcome with three or more unordered categories by placing prior distributions over the regression coefficients and updating them with data via Bayes' theorem. The result is a full posterior distribution over category probabilities for each observation, enabling principled uncertainty quantification and regularization through the prior. | Ordinal logistic regression — most commonly the proportional-odds model — estimates the relationship between one or more predictors and an ordered categorical outcome (e.g., Likert scales, disease severity grades, educational attainment levels). It models cumulative log-odds across the ordered categories while assuming a single shared effect of each predictor at all thresholds. |
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