Bayesian Data Analysis

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis. CRC Press.

Type

Book chapter

Intellectual base

Review or monograph

Year

2013

Link

https://sites.stat.columbia.edu/gelman/book/

Cited from

cognitive_science, statistics_experimental_design

Cited by

4 citations across 4 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Bayesian Updating
  • The iterative update structure across observations

    This sourceCanonical Bayesian reference: develops sequential/iterative updating (posterior becomes prior for the next datum), credible intervals, hierarchical models, and Bayesian adaptive designs (dose-finding, interim analysis, early stopping). SUPPORTS markers 005 (iterative update structure), 008 (adaptive Bayesian trial designs), and 012 (credible intervals + sequential evidence integration as operational decision support).

• Measurement Uncertainty and Observational Noise
  • Interpreting results requires Bayesian reasoning: the measured value (e.g., a positive test) depends on both the true state (disease present or absent) and the test's noise characteristics (false positive and false negative rates), a posterior-update logic Gelman et al. (2013) develop systematically in their canonical Bayesian Data Analysis treatment.

    This sourceCanonical Bayesian reference: develops posterior inference (including diagnostic-test interpretation) that combines prior probability of true state with likelihood under known measurement noise characteristics (sensitivity, specificity).

• Precision Weighting
  • In Bayesian inference, Gaussian posterior beliefs combine prior and likelihood by precision.

    This sourceDerives the Gaussian posterior mean as the precision-weighted average of prior and likelihood, with precisions adding.

Domain-specific

• Prior Probability
  • 'Before' is inferential rather than necessarily chronological: the prior may encode previous studies, expert information, structural constraints, or a deliberately weak baseline, but it is the distribution not yet conditioned on the dataset currently under analysis.

Verification

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Links previously used in the corpus

Before the registry existed this work was also linked 1 other way.

• https://www.routledge.com/Bayesian-Data-Analysis/Gelman-Carlin-Stern-Dunson-Vehtari-Rubin/p/book/9781439840955 ×1

Registry ID ref:169abbfe9381 · see in the full table