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Cited by 27,276Statistical Methods and Bayesian InferenceMarkov Chains and Monte Carlo MethodsBayesian Modeling and Causal Inference

Bayesian Data Analysis

Gelman, Andrew, Carlin, John B., Stern, Hal S., Dunson, David B., Vehtari, Aki, Rubin, Donald B.·2013·Chapman & Hall/CRC eBooks
DOI 10.1201/b16018
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Abstract

Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Using examples largely from the authors' own experiences, the book focuses on modern computational tools and obtains inferences using computer simulations. Its unique features include thorough discussions of the methods for

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Crossref · 10.1201/b16018

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4 of 4 references verified0 citation errors · 0 not checkable

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  • Bayesian methods' capacity to handle uncertainty, incorporate prior knowledge, and update beliefs based on evidence makes them effective for modeling complex systems.

    “Their capacity to handle uncertainty, incorporate prior knowledge, and update beliefs based on evidence makes them effective for modeling complex systems (Gelman et al. 1995; MacKay 2003).”

    From The myth of the Bayesian brain (2025) · cites this paper as Gelman et al. 1995

  • Bayesian statistics have the advantage of allowing researchers to hypothesize null effects.

    “We adopt Bayesian statistics for its advantages in being able to hypothesize null effects[27,73].”

    From Explanations Can Reduce Overreliance on AI Systems During Decision-Making (2023) · cites this paper as [27]

  • Using a Hamiltonian Monte Carlo algorithm and a hierarchical setup improves the efficiency of the estimation procedure.

    “To improve the efficiency of this procedure, I use a Hamiltonian Monte Carlo algorithm (Gelman et al. 2013) and employ a hierarchical setup that considers each of the four sets of parameters as draws from four common population distributions:”

    From Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data (2015) · cites this paper as Gelman et al. 2013

  • Because the proposed model is a Bayesian extension of a conventional MFRM, its parameter estimates approach those of a non-Bayesian conventional MFRM when there is a large amount of data.

    “Because this model is a Bayesian extension of a conventional MFRM, its parameter estimates approach those of a non-Bayesian conventional MFRM when we have a large amount of data, which is a desirable property.”

    From A Bayesian many-facet Rasch model with Markov modeling for rater severity drift (2022) · cites this paper as Gelman et al., 2013

  • The parameter estimates of a Bayesian model approach those of its non-Bayesian counterpart as the amount of data increases because the influence of the prior distribution decreases.

    “The parameter estimates of a Bayesian model are known to approach those of its non-Bayesian counterpart as the amount of data increases. This is because the influence of the prior distribution decreases (Gelman et al., 2013).”

    From A Bayesian many-facet Rasch model with Markov modeling for rater severity drift (2022) · cites this paper as Gelman et al., 2013

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Authors
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B.
Affiliations
Royal Children's Hospital (AU)
ORCID
Venue
Chapman & Hall/CRC eBooks
Publisher
Chapman and Hall/CRC
Published
2013-11-27
DOI
10.1201/b16018
OpenAlex
W2045656233
Cited by
27,276
References
4
Citations by year
2026: 1089 · 2025: 1536 · 2024: 1370 · 2023: 1223 · 2022: 1481 · 2021: 1849 · 2020: 1770 · 2019: 1645 · 2018: 1347 · 2017: 1184 · 2016: 1184 · 2015: 1171 · 2014: 1048 · 2013: 1060 · 2012: 1052
FWCI
48.90
Type
book
Language
en
Open access
closed
Locations
  • Chapman & Hall/CRC eBooks (publishedVersion)
  • CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords
Bayesian analysis, Bayesian inference, computational tools, computer simulation, statistical modeling, model criticism

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