# Visualization in Bayesian workflow

arXiv: https://arxiv.org/abs/1709.01449 · HTML: https://mlsanity.com/paper/1709.01449/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

## Built on by (most cited first, 25 of 25)

- [Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks](https://mlsanity.com/paper/2112.08866/) (2021)
- [Model Criticism for Long-Form Text Generation](https://mlsanity.com/paper/2210.08444/) (2022)
- [Amortized Bayesian Multilevel Models](https://mlsanity.com/paper/2408.13230/) (2024)
- [Simulation-based Bayesian inference under model misspecification](https://mlsanity.com/paper/2503.12315/) (2025)
- [BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python](https://mlsanity.com/paper/2602.07098/) (2026)
- [Toward a Taxonomy of Trust for Probabilistic Machine Learning](https://mlsanity.com/paper/2112.03270/) (2021)
- [Are you using test log-likelihood correctly?](https://mlsanity.com/paper/2212.00219/) (2022)
- [Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference](https://mlsanity.com/paper/2301.13368/) (2023)
- [Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation](https://mlsanity.com/paper/2308.01054/) (2023)
- [Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation](https://mlsanity.com/paper/2406.03154/) (2024)
- [Measuring the robustness of Gaussian processes to kernel choice](https://mlsanity.com/paper/2106.06510/) (2021)
- [Simulation-based Inference with the Python Package sbijax](https://mlsanity.com/paper/2409.19435/) (2024)
- [A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms](https://mlsanity.com/paper/2204.06043/) (2022)
- [Efficient identification of informative features in simulation-based inference](https://mlsanity.com/paper/2210.11915/) (2022)
- [Graph Structure Learning with Interpretable Bayesian Neural Networks](https://mlsanity.com/paper/2406.14786/) (2024)
- [Amortized Bayesian Mixture Models](https://mlsanity.com/paper/2501.10229/) (2025)
- [Posterior SBC: Simulation-Based Calibration Checking Conditional on Data](https://mlsanity.com/paper/2502.03279/) (2025)
- [Discriminative calibration: Check Bayesian computation from simulations and flexible classifier](https://mlsanity.com/paper/2305.14593/) (2023)
- [Bayesian NVH metamodels to assess interior cabin noise using measurement databases](https://mlsanity.com/paper/2207.02120/) (2022)
- [Bayesian Calibration of MEMS Accelerometers](https://mlsanity.com/paper/2306.06144/) (2023)
- [Expert-elicitation method for non-parametric joint priors using normalizing flows](https://mlsanity.com/paper/2411.15826/) (2024)
- [Amortized Bayesian Inference on Multilevel Models of Arbitrary Structure](https://mlsanity.com/paper/2609.40024/) (2026)
- [A tutorial on the Bayesian statistical approach to inverse problems](https://mlsanity.com/paper/2304.07610/) (2023)
- [Hierarchical Bayesian Crowdsourcing with Item Difficulty](https://mlsanity.com/paper/2405.19521/) (2024)
- [Differential Privacy of Gaussian Process Posterior Sampling](https://mlsanity.com/paper/2606.17995/) (2026)

## Abstract (arXiv)

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.