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When building statistical models for Bayesian data analysis tasks, required and optional iterative adjustments and different modelling choices can give rise to numerous candidate models.
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Gelfand, A. E. (1995) · 1995
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Gaussian Processes for Machine Learning
Rasmussen, C. E. and Williams, C. K. I. (2005) · 2005
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Boba: Authoring and Visualizing Multiverse Analyses
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Uncertainty in Bayesian leave-one-out cross-validation based model comparison
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The horseshoe estimator for sparse signals
Carvalho, C. M., Polson, N. G., and Scott, J. G. (2010) · 2010
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Handbook of Markov Chain Monte Carlo
Brooks, S., Gelman, A., Jones, G., and Meng, X.-L., editors (2011) · 2011
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Gelman, A., Vehtari, A., Simpson, D., Margossian, C. C., Carpenter, B., Yao, Y., Kennedy, L., Gabry, J., Bürkner, P.-C., and Modrák, M. (2020) · 2011
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Influence of Valentine’s Day and Halloween on Birth Timing
Levy, B. R., Chung, P. H., and Slade, M. D. (2011) · 2011
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Philosophy and the Practice of Bayesian Statistics in the Social Sciences
Gelman, A. and Shalizi, C. R. (2012) · 2012
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A survey of Bayesian predictive methods for model assessment, selection and comparison
Vehtari, A. and Ojanen, J. (2012) · 2012
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Bayesian Data Analysis
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The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time
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The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
Hoffman, M. D. and Gelman, A. (2014) · 2014
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Factorial Comparison of Working Memory Models
van den Berg, R., Awh, E., and Ma, W. J. (2014) · 2014
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Assessment of vibration of effects due to model specification can demonstrate the instability of observational associations
Patel, C. J., Burford, B., and Ioannidis, J. P. (2015) · 2015
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loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models
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Bayesian Modeling and Computation in Python
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Implicitly adaptive importance sampling
Paananen, T., Piironen, J., Bürkner, P.-C., and Vehtari, A. (2021) · 2021
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multiverse: Multiplexing Alternative Data Analyses in R Notebooks
Sarma, A., Kale, A., Moon, M. J., Taback, N., Chevalier, F., Hullman, J., and Kay, M. (2021) · 2021
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Rank-normalization, folding, and localization: An improved $\widehat{R}$ for assessing convergence of MCMC (with discussion)
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Difficulty of selecting among multilevel models using predictive accuracy
Wang, W. and Gelman, A. (2015) · 2015
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Diagnosing Suboptimal Cotangent Disintegrations in Hamiltonian Monte Carlo
Betancourt, M. (2016) · 2016
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What is Modern Statistical Workflow?
Savage, J. (2016) · 2016
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Increasing Transparency Through a Multiverse Analysis
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Julia: A fresh approach to numerical computing
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V. B. (2017) · 2017
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brms: An R Package for Bayesian Multilevel Models Using Stan
Bürkner, P.-C. (2017) · 2017
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Stan: A Probabilistic Programming Language
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., and Riddell, A. (2017) · 2017
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Bell, S. J., Kampman, O. P., Dodge, J., and Lawrence, N. D. (2022) · 2022
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Multi-Model Probabilistic Programming
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bayesplot: Plotting for Bayesian Models
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A Survey of Tasks and Visualizations in Multiverse Analysis Reports
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Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
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Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and multiple sample comparison
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Abstractions for Probabilistic Programming to Support Model Development
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A Cheat Sheet for Bayesian Prediction
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ggdist: Visualizations of Distributions and Uncertainty
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One data set, many analysts: Implications for practicing scientists
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Robust and efficient projection predictive inference
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Efficient estimation and correction of selection-induced bias with order statistics
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Using reference models in variable selection
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projpred: Projection predictive feature selection
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R: A Language and Environment for Statistical Computing
R Core Team (2023) · 2023
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Scholz, M. and Bürkner, P.-C. (2023) · 2023
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Stan Modeling Language Users Guide and Reference Manual 2.31
Stan Development Team (2023) · 2023
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Margossian, C. C., Hoffman, M. D., Sountsov, P., Riou-Durand, L., Vehtari, A., and Gelman, A. (2024) · 2024
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Pareto smoothed importance sampling
Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024) · 2024
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