Bayesian Visualization Cheat Sheet for Posterior Diagnostics
A Chinese Bayesian-analysis reference for posterior distributions, intervals, chain diagnostics, parameter relationships, predictive checks and model comparison.
What this reference covers
This reference sheet organizes Bayesian graphics by diagnostic question: chain behavior, posterior uncertainty, parameter relationships, posterior predictive checks and model comparison. The main skill is choosing a graphic that preserves sample structure and uncertainty, not selecting a plot because it looks polished.
Validate the graphic before the conclusion
Begin with simulated samples whose behavior is understood. Check axes, intervals, groups, transformations, reference values and units, then move to the fitted output. Keep chains, warmup, iterations, parameter names and missing values visible when they affect interpretation.
Connect plots to model evidence
A posterior plot does not establish convergence, causality or predictive validity on its own. Pair it with sampling summaries, prior sensitivity, model assumptions, posterior predictive checks and domain review. A caption should state sample, interval definition, transformation and diagnostic purpose.
Maintenance note
Model interfaces, object structures, samplers and package versions can change. Preserve code, priors, samples, seed and environment with the rendered figure. Content review date: 2026-08-23.
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Bayesian visualization and diagnostics study guide
Name the diagnostic question, test the plotting call on simulated samples, preserve chain and parameter structure, and connect every figure to numerical and domain checks.
Before you start
- Prepare a PDF reader, an R environment and a minimal fitted model or simulated posterior sample.
- Decide whether the goal is chain diagnosis, posterior uncertainty, parameter comparison, prediction or model comparison.
- Record sampler, chains, iterations, warmup, parameter names and seed.
Quick start
- 01
Classify the diagnostic goal
Separate chain behavior, posterior distribution, parameter relationship, predictive check and model-comparison questions.
- 02
Test with simulated samples
Run one graphic on a known simulated sample and verify axes, intervals, grouping, colors and transformations.
- 03
Check dimensions and chains
Confirm chain count, iterations, warmup, parameter names and missing values before combining or faceting samples.
- 04
Write the evidence caption
State sample, interval, transformation and diagnostic purpose, and record which questions the figure leaves open.
Usage tips
- A polished posterior plot does not establish convergence or causality by itself.
- Units, reference values, intervals and transformations should be visible in the figure or caption.
- Validate interface and object structure with a small sample before rendering a full model report.
Troubleshooting and uninstall
Why does the plotting function reject the object?
Check whether the input is posterior samples, a fitted object or predictive output, then verify parameter names, chain dimensions and transformations.
Why does a normal-looking diagnostic still leave model concerns?
Combine the graphic with sampling summaries, prior sensitivity, assumptions, predictive checks and domain review instead of treating one figure as proof.
Frequently asked questions
Which Bayesian graphics are included?
The reference covers posterior distributions, intervals, chain diagnostics, parameter relationships, predictive checks and model comparison.
Does the sheet include a ready-to-run Bayesian model?
It is mainly a graphics and function reference; data, model and execution environment must be prepared separately.
Can a posterior plot prove a model is correct?
No. Review sampling diagnostics, priors, assumptions, predictive checks and domain evidence together.
What should every published figure record?
Record the sample, interval definition, transformation, model version, diagnostic purpose and relevant uncertainty interpretation.