Getting It Right

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High‐dimensional coexistence based on individual variation: a synthesis of evidence (2010, background): “Large models can suffer from a range of problems, involving the underlying model, computation, and the specific data sets (Geweke 2004, Cressie et al. 2009), which information criteria do not address.”
Monte Carlo Gradient Estimation in Machine Learning (2019, unknown): “batch size B= 32;N= 50. We observed similar variance reduction with respect to for all but the measure-valued estimator, where coupling had no eect on gradient variance. e.g. the tests described by Geweke (2004). For all the tests accompanying our implementation of the stochastic gradient estimators”
Sandwiching the marginal likelihood using bidirectional Monte Carlo (2015, methodology): “To test the MCMC algorithms themselves, we used the Geweke test (Geweke, 2004).”
RISK OF BAYESIAN INFERENCE IN MISSPECIFIED MODELS, AND THE SANDWICH COVARIANCE MATRIX (2013, background): “2 of Hall and Heyde (1980) and the so-called Cramer-Wold device.”
POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES (2011, methodology): “To check the correctness of the algorithm design and implementation we used the joint distribution tests from Geweke (2004) and related tests from Cook, Gelman, and Rubin (2006).”