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The Experiment is just as Important as the Likelihood in Understanding the Prior: a Cautionary Note on Robust Cognitive Modeling, , и . Computational Brain & Behavior, 2 (3): 210--217 (01.12.2019)Asynchronous Gibbs Sampling., , и . AISTATS, том 108 из Proceedings of Machine Learning Research, стр. 144-154. PMLR, (2020)Constructing Priors that Penalize the Complexity of Gaussian Random Fields, , , и . (2015)cite arxiv:1503.00256.Hamiltonian Monte Carlo using an embedded Laplace approximation, , , и . (2020)cite arxiv:2004.12550Comment: 16 pages, 12 figures.Multivariate Gaussian Random Fields Using Systems of Stochastic Partial Differential Equations, , , и . (2013)cite arxiv:1307.1379Comment: 47 pages, 19 figures.The Prior Can Often Only Be Understood in the Context of the Likelihood., , и . Entropy, 19 (10): 555 (2017)Validating Bayesian Inference Algorithms with Simulation-Based Calibration, , , , и . (2018)cite arxiv:1804.06788Comment: 26 pages, 14 figures.Bayesian computing with INLA: New features., , , и . Comput. Stat. Data Anal., (2013)Using stacking to average Bayesian predictive distributions, , , и . (2017)cite arxiv:1704.02030.The Integrated Nested Laplace Approximation for Fitting Dirichlet Regression Models., , , , и . J. Comput. Graph. Stat., 32 (3): 805-823 (2023)