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领域研究设计统计学
方法族Process / pipelineBayesian methods
起源年份1990s–2000s (systematic application to observational research)1763
提出者Thomas Bayes (foundational theorem, 1763); modern applied form developed by Sander Greenland, Andrew Gelman, and colleagues (1990s–2000s)Thomas Bayes; Pierre-Simon Laplace
类型Quantitative non-experimental research design with Bayesian inferenceProbabilistic inference paradigm
开创性文献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: 978-1439840955Bayes, 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 ↗
别名Bayesian observational study, Bayesian non-experimental quantitative design, Bayesian causal observational analysis, BOQRBayes inference, Bayesian statistics, Bayesian updating, posterior inference
相关43
摘要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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ScholarGate方法对比: Bayesian Observational Quantitative Research · Bayesian Inference. 于 2026-06-17 检索自 https://scholargate.app/zh/compare