amortized-workflow

Amortized Bayesian Workflow

Workflow overview

Every amortized Bayesian analysis follows this sequence. Do not skip steps — especially simulator validation and model criticism.

1. Formulate — Define the generative story. What latent variables or parameters generated the observations?
2. Specify the simulator regime — The first iteration always uses offline training for fast turnaround, regardless of simulator speed. The simulator regime only determines the simulation budget for the pilot run:
   • Fast simulator (< 0.05 s per draw): pre-simulate 20 000 datasets, train for 100 epochs
   • Slow simulator (> 1 s – minutes per draw): pre-simulate 3 000–5 000 datasets, train for 100 epochs
   • No simulator / pre-existing bank: use whatever is available; switch to disk training if it does not fit in memory Online training is a refinement step — use it only after the first offline pass shows healthy diagnostics and you want to squeeze out more performance.
3. Define prior + observation model or simulation bank
   • Implement prior and observation model and wrap them in a simulator
   • Pre-simulate the pilot budget into a dict (using workflow.simulate(N)) for offline training
   • If the simulator is external or proprietary, ensure simulations are already generated from the intended prior and data-generating process
4. Choose the architecture — this step is critical; getting it wrong ruins inference. See references/conditioning.md for the full conditioning logic and decision table.
   • "Simple vector" means the observation is a single fixed-length feature vector whose element order is meaningful (e.g., 5 named sensor readings, a pre-computed summary statistic). Only then: route through inference_conditions with no summary network.
   • Set-based / exchangeable data — If the simulator produces N observations that are exchangeable, the data is a set, not a vector. This includes: N i.i.d. draws, regression datasets with (x, y) pairs, repeated measurements, trial-level data, cross-sectional samples. Route through summary_variables with a SetTransformer. Never put this in inference_conditions.
   • Time series — ordered sequences: route through summary_variables with TimeSeriesTransformer or TimeSeriesNetwork.

• Images as conditions / observations for parameter inference — route through summary_variables with ConvolutionalNetwork.
• Images as inferential targets — conditional image generation, spatial field generation, denoising, and other image-valued outputs require an image-capable diffusion inference network. Use bf.networks.DiffusionModel(subnet=...) with UNet, UViT, or ResidualUViT; see references/image-generation.md.
• A workflow can use both slots simultaneously. Fixed-length metadata (e.g., sample size N, scalar design variables) can go in inference_conditions while structured observations go in summary_variables.
• When in doubt, use a summary network. It is always safer to include one than to omit one; a summary network will always be needed if the data has more than one axis.

1. Build the workflow — Prefer bf.BasicWorkflow(...)
   • Decide on which variables to auto-standardize. Prefer standardize="all" unless you have verfied that the simulator outputs are already in a good range for the networks.
2. Run simulation sanity checks — Before training, verify that simulated data look plausible and span the relevant range of real observations. Again, pay attention to what needs to be standardized.
3. Train the amortizer — First iteration always uses offline training for fast feedback:
   • workflow.fit_offline(...) with the pre-simulated pilot budget (default first pass)
   • workflow.fit_online(...) only as a refinement step after offline diagnostics look healthy, or when the user explicitly requests it
   • workflow.fit_disk(...) if streaming simulations from disk Always offer to run training in the terminal so the user can monitor progress interactively.
4. Diagnose in silico — Use held-out simulations with known ground truth using the workflow's built-in diagnostics: workflow.compute_default_diagnostics(...) for numerical results and workflow.plot_default_diagnostics(...) for visual diagnostics.
5. Amortized inference on real data — Use workflow.sample(...)
6. Posterior predictive checks (PPCs) — Re-simulate data from posterior samples and compare to the real data using model-specific test quantities
7. Write a report — Use references/reporting.md to generate a structured report outlining results and next steps.