เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| Bayesian ANOVA× | Markov Chain Monte Carlo (MCMC)× | |
|---|---|---|
| สาขาวิชา | เบย์ | เบย์ |
| ตระกูล | Bayesian methods | Bayesian methods |
| ปีกำเนิด≠ | 2012 | — |
| ผู้ริเริ่ม≠ | Rouder, Morey, Speckman & Province | — |
| ประเภท≠ | Bayesian hypothesis test / group comparison | Posterior sampling algorithm |
| แหล่งต้นตำรับ≠ | Rouder, J. N., Morey, R. D., Speckman, P. L. & Province, J. M. (2012). Default Bayes Factors for ANOVA Designs. Journal of Mathematical Psychology, 56(5), 356–374. DOI ↗ | 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 |
| ชื่อเรียกอื่น≠ | bayesian analysis of variance, bayes factor ANOVA, JZS ANOVA, Bayesçi ANOVA — Bayes Faktörü ile Grup Karşılaştırması | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| ที่เกี่ยวข้อง≠ | 4 | 3 |
| สรุป≠ | Bayesian ANOVA, formalised by Rouder, Morey, Speckman and Province (2012), tests whether group means differ by quantifying the evidence for the alternative hypothesis relative to the null using the Bayes Factor (BF₁₀). Unlike classical ANOVA, it can also measure evidence in favour of the null hypothesis, making it equally informative when groups do not differ. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
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