Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Cercetare privind testarea ipotezelor bayesiene× | Cercetare privind testarea modelelor bayesiene× | |
|---|---|---|
| Domeniu | Design de cercetare | Design de cercetare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1935–1961 (Jeffreys); extended by Kass & Raftery 1995, Wagenmakers 2007–2010 | 1935 (Jeffreys); widely adopted in social and behavioral sciences from the 1990s onward |
| Autorul original≠ | Harold Jeffreys (formal Bayes factor framework) | Harold Jeffreys; formalized for applied sciences by Robert Kass and Adrian Raftery |
| Tip≠ | Quantitative research design | Quantitative inferential research design |
| Sursa seminală≠ | Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press. ISBN: 978-0198503682 | Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795. DOI ↗ |
| Denumiri alternative | Bayesian significance testing, Bayes factor hypothesis testing, BHT research, Bayesian inference testing | Bayesian hypothesis testing, Bayesian model comparison, Bayes factor analysis, BMT |
| Înrudite≠ | 5 | 4 |
| Rezumat≠ | Bayesian hypothesis testing research is a quantitative design in which competing hypotheses are evaluated by updating prior beliefs with observed data to produce posterior probabilities and Bayes factors. Unlike frequentist null-hypothesis significance testing, it quantifies the relative evidence for each hypothesis, supports optional stopping, and allows accumulation of evidence across studies without inflating Type I error rates. | Bayesian model testing research is a quantitative design in which competing theoretical models or hypotheses are evaluated by comparing their marginal likelihoods given observed data. The central tool is the Bayes factor — a ratio that quantifies how much more likely the data are under one model than under another. Unlike null-hypothesis significance testing, Bayesian model testing yields direct evidence for or against specific hypotheses, incorporates prior knowledge, and can support a null hypothesis rather than merely failing to reject it. |
| ScholarGateSet de date ↗ |
|
|