Bayesian data analysis, third edition

By Andrew Gelman, J. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin. 2013

4,900 citations recorded by the source corpus.

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Citations in the corpus

Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “Using prior information [46] can help specify a realistic utility function.”
Stan: A Probabilistic Programming Language (2017, methodology): “This supplies fairly diﬀuse starting points when transformed back to the constrained scale, and thus help with convergence diagnostics as discussed in (Gelman et al. 2013).”
A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, background): “We note that while MCMC convergence should ideally be examined based on all model parameters, the convergence should be checked at least for the key model parameters to be used in subsequent inference (Gelman et al. 2013).”
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale (2019, methodology): “We establish the convergence of the RMH posterior by running two independent MCMC chains with different initializations and computing the the Gelman–Rubin convergence metric [24] to confirm that they converge onto the same posterior distribution (Section 4.”
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery (2021, background): “In any of the above scenarios, it is desirable to obtain an explicit posterior distribution over the unobserved DAG instead of a single point estimate [19].”
Mining idioms from source code (2014, background): “Bayesian statistics [15, 37] is alternative general framework to classical frequentist statistical methods, such as confidence intervals and hypothesis testing, that allows the analyst to encode prior knowledge about the quantity of interest.”
The NANOGrav 15 yr Data Set: Evidence for a Gravitational-wave Background (2023, background): “In addition to Bayes factors, the goodness of fit of probabilistic models can be evaluated by assessing their predictive performance (Gelman et al. 2013).”
Pathfinder: Parallel quasi-Newton variational inference (2021, unknown): “We follow Gelman et al. (2013) in analyzing the number of births per day in the United States from 1969 through 1988 with Gaussian processes (GP).”
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (2015, methodology): “repeated 10-fold-CV was not feasible for the radon example due to the computation time required. 19 Monte Carlo standard errors arising from the nite number of simulation draws using the formula from Gelman et al. (2013) which uses both between and within-chain information and is implemented in Stan”
Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package (2016, methodology): “The hierarchical structure of HBA leads to “shrinkage” effects (Gelman et al., 2013) in individual estimates.”
Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data (2014, unknown): “, [7]):”
Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions (2020, unknown): “[3, 36]), especially for models employed for predictions and forecasts.”