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| Nghiên cứu định lượng quan sát Bayes× | Suy luận Bayes× | |
|---|---|---|
| Lĩnh vực≠ | Thiết kế nghiên cứu | Thống kê |
| Họ≠ | Process / pipeline | Bayesian methods |
| Năm ra đời≠ | 1990s–2000s (systematic application to observational research) | 1763 |
| Người khởi xướng≠ | Thomas Bayes (foundational theorem, 1763); modern applied form developed by Sander Greenland, Andrew Gelman, and colleagues (1990s–2000s) | Thomas Bayes; Pierre-Simon Laplace |
| Loại≠ | Quantitative non-experimental research design with Bayesian inference | Probabilistic inference paradigm |
| Công trình gốc≠ | 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 | Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418. link ↗ |
| Tên gọi khác≠ | Bayesian observational study, Bayesian non-experimental quantitative design, Bayesian causal observational analysis, BOQR | Bayes inference, Bayesian statistics, Bayesian updating, posterior inference |
| Liên quan≠ | 4 | 3 |
| Tóm tắt≠ | Bayesian observational quantitative research applies Bayesian statistical inference to data collected without experimental manipulation — surveys, administrative records, registries, or secondary datasets. Instead of relying solely on p-values and confidence intervals, the analyst encodes prior knowledge about parameters as probability distributions, updates them with observed data via Bayes' theorem, and reports conclusions as posterior probability statements. The approach is especially valued in epidemiology, social science, and health services research where randomisation is impossible or unethical. | Bayesian inference is a statistical paradigm in which probability represents degrees of belief rather than long-run frequencies. It encodes prior knowledge about parameters in a prior distribution, combines that prior with the likelihood of observed data via Bayes' theorem, and produces a posterior distribution that quantifies updated uncertainty. The foundational theorem was published posthumously by Thomas Bayes in 1763 and subsequently systematized by Pierre-Simon Laplace in his 1812 Théorie analytique des probabilités. |
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