Visualization in Bayesian workflow
arXiv:1709.01449 · doi:10.1111/rssa.12378
Abstract
Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high-dimensional models that are used by applied researchers.
17 pages, 11 Figures. Includes supplementary material
References in corpus (3)
Cited by in corpus (57)
- Bayesian Item Response Modeling in R with brms and Stan
- Projective Inference in High-dimensional Problems: Prediction and Feature Selection
- Bayesian Data Analysis in Empirical Software Engineering Research
- Can visualization alleviate dichotomous thinking? Effects of visual representations on the cliff effect
- GRB Fermi-LAT afterglows: explaining flares, breaks, and energetic photons
- Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity
- Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality
- Imaging the 511 keV positron annihilation sky with COSI
- Tea: A High-level Language and Runtime System for Automating Statistical Analysis
- A Physical Background Model for the Fermi Gamma-ray Burst Monitor
- DFSeer: A Visual Analytics Approach to Facilitate Model Selection for Demand Forecasting
- Not All Requirements Prioritization Criteria Are Equal at All Times: A Quantitative Analysis
- Limitations of "Limitations of Bayesian leave-one-out cross-validation for model selection"
- The Risks of Ranking: Revisiting Graphical Perception to Model Individual Differences in Visualization Performance
- Bayesian Calibration of MEMS Accelerometers
- Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
- Holes in Bayesian Statistics
- A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms
- A tutorial on the Bayesian statistical approach to inverse problems
- nazgul: A statistical approach to gamma-ray burst localization. Triangulation via non-stationary time-series models
- The fine-scale structure of polar coronal holes
- Bayesian Pharmacokinetic Modeling of Dynamic Contrast-Enhanced Magnetic Resonance Imaging: Validation and Application
- Applying Meta-Analytic-Predictive Priors with the R Bayesian evidence synthesis tools
- Bayes-raking: Bayesian Finite Population Inference with Known Margins
- Modeling Longitudinal Dynamics of Comorbidities
- ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference with and without Covariate Information
- Identifying supermassive black hole recoil in elliptical galaxies
- Accelerating delayed-acceptance Markov chain Monte Carlo algorithms
- DBNets2.0: simulation-based inference for planet-induced dust substructures in protoplanetary discs
- GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables
- A Flexible Multi-Metric Bayesian Framework for Decision-Making in Phase II Multi-Arm Multi-Stage Studies
- Challenging common interpretability assumptions in feature attribution explanations
- BayesTime: Bayesian Functional Principal Components for Sparse Longitudinal Data
- Graphical outputs and Spatial Cross-validation for the R-INLA package using INLAutils
- Translating predictive distributions into informative priors
- Combining chains of Bayesian models with Markov melding
- Stochastic Convergence Rates and Applications of Adaptive Quadrature in Bayesian Inference
- Productivity equation and the m distributions of information processing in workflows
- Bayesian Mendelian Randomization identifies disease causing proteins via pedigree data, partially observed exposures and correlated instruments
- A Latent Variable Model for Relational Events with Multiple Receivers
- stanhf: HistFactory models in the probabilistic programming language Stan
- Modeling racial/ethnic differences in COVID-19 incidence with covariates subject to non-random missingness
- A noisy-input generalised additive model for relative sea-level change along the Atlantic coast of North America
- Mapping poverty at multiple geographical scales
- Bayesian models for survival data of clinical trials: Comparison of implementations using R software
- Hierarchical spline for time series forecasting: An application to Naval ship engine failure rate
- Deep inference of simulated strong lenses in ground-based surveys
- Prediction can be safely used as a proxy for explanation in causally consistent Bayesian generalized linear models
- Transforming Probabilistic Programs for Model Checking
- A stratified age-period-cohort model for spatial heterogeneity in all-cause mortality
- Skills to not fall behind in school
- Bayesian Analysis of Formula One Race Results: Disentangling Driver Skill and Constructor Advantage
- Unfolding the Network of Peer Grades: A Latent Variable Approach
- ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in {R}
- Spatial modelling of COVID-19 incident cases using Richards' curve: an application to the Italian regions
- Bayesian model selection in the -open setting -- Approximate posterior inference and probability-proportional-to-size subsampling for efficient large-scale leave-one-out cross-validation
- Treatment effect estimation with Multilevel Regression and Poststratification