# sbi: Simulation-Based Inference

> sbi is a PyTorch-based Python package for simulation-based inference (SBI).
> It estimates full Bayesian posterior distributions over simulator parameters
> from observed data, without requiring access to the simulator's likelihood
> function. Methods include Neural Posterior Estimation (NPE), Neural Likelihood
> Estimation (NLE), Neural Ratio Estimation (NRE), and flow/score matching (FMPE, NPSE).

sbi provides both a simple one-line interface (`sbi.inference.infer`) and a
flexible class-based interface (`NPE`, `NLE`, `NRE`, `FMPE`). The typical
workflow is: define a prior and simulator, generate simulations, train a neural
density estimator, build a posterior, and validate with diagnostics (SBC,
expected coverage, TARP, L-C2ST).

## Tutorials

- [Getting Started](https://sbi.readthedocs.io/en/latest/tutorials/00_getting_started.html): Minimal NPE example from prior to posterior
- [Bayesian Workflow](https://sbi.readthedocs.io/en/latest/tutorials/01_Bayesian_workflow.html): Full workflow with diagnostics and analysis
- [Implemented Methods](https://sbi.readthedocs.io/en/latest/tutorials/16_implemented_methods.html): All NPE, NLE, NRE, FMPE variants with code
- [Vector Field Methods](https://sbi.readthedocs.io/en/latest/advanced_tutorials/19_vector_field_methods.html): FMPE and NPSE — flow matching and score matching for SBI

## How-To Guides

- [Custom Priors](https://sbi.readthedocs.io/en/latest/how_to_guide/00_custom_prior.html): MultipleIndependent, process_prior, non-torch priors
- [Summary Statistics](https://sbi.readthedocs.io/en/latest/how_to_guide/01_crafting_summary_statistics.html): Hand-crafted features for high-dim data
- [Multi-Round Inference](https://sbi.readthedocs.io/en/latest/how_to_guide/02_multiround_inference.html): Sequential methods (SNPE, SNLE, SNRE)
- [Density Estimators](https://sbi.readthedocs.io/en/latest/how_to_guide/03_density_estimators.html): Choosing and configuring neural networks
- [Embedding Networks](https://sbi.readthedocs.io/en/latest/how_to_guide/04_embedding_networks.html): FCEmbedding, CNNEmbedding, custom nn.Module
- [Choosing Inference Method](https://sbi.readthedocs.io/en/latest/how_to_guide/06_choosing_inference_method.html): NPE vs NLE vs NRE decision guide
- [Sampler Interface](https://sbi.readthedocs.io/en/latest/how_to_guide/09_sampler_interface.html): Choosing MCMC, rejection, VI or importance sampling for NLE/NRE posteriors
- [Variational Inference](https://sbi.readthedocs.io/en/latest/how_to_guide/26_variational_inference.html): Fixed-observation and amortized VI, quality checks, SIR refinement
- [i.i.d. Observations](https://sbi.readthedocs.io/en/latest/how_to_guide/11_iid_sampling_with_nle_or_nre.html): Multiple observations with NLE/NRE
- [Hyperparameter Tuning](https://sbi.readthedocs.io/en/latest/how_to_guide/21_hyperparameter_tuning.html): Optuna integration for architecture search
- [Abstraction Levels](https://sbi.readthedocs.io/en/latest/how_to_guide/24_abstraction_levels.html): Four levels of control over the density estimator (Trainer classes to custom `nn.Module`), plus sampling-control levels for NLE
- [FMPE and NPSE Recommendations](https://sbi.readthedocs.io/en/latest/how_to_guide/25_choosing_vector_field_options.html): Practical guide for choosing between vector field methods and configuring them

## Diagnostics

- [Choosing a Diagnostic](https://sbi.readthedocs.io/en/latest/how_to_guide/14_choose_diagnostic_tool.html): Which validation tool to use
- [Expected Coverage](https://sbi.readthedocs.io/en/latest/how_to_guide/15_expected_coverage.html): Joint posterior calibration check
- [Simulation-Based Calibration](https://sbi.readthedocs.io/en/latest/how_to_guide/16_sbc.html): Per-parameter marginal calibration
- [TARP](https://sbi.readthedocs.io/en/latest/how_to_guide/17_tarp.html): Calibration without density evaluation
- [L-C2ST](https://sbi.readthedocs.io/en/latest/how_to_guide/13_diagnostics_lc2st.html): Per-observation local coverage test
- [Model Misspecification](https://sbi.readthedocs.io/en/latest/how_to_guide/18_model_misspecification.html): Detecting if x_o is outside simulator range

## Advanced Topics

- [Time Series Embeddings](https://sbi.readthedocs.io/en/latest/how_to_guide/20_time_series_embedding.html): CausalCNN, Transformer for temporal data
- [Restriction Estimator](https://sbi.readthedocs.io/en/latest/advanced_tutorials/06_restriction_estimator.html): Handling invalid simulations (NaN/Inf)
- [Sensitivity Analysis](https://sbi.readthedocs.io/en/latest/advanced_tutorials/07_sensitivity_analysis.html): ActiveSubspace for parameter importance
- [Posterior Parameters](https://sbi.readthedocs.io/en/latest/how_to_guide/19_posterior_parameters.html): Dataclass-based sampler configuration
- [Experiment Tracking](https://sbi.readthedocs.io/en/latest/how_to_guide/22_experiment_tracking.html): TensorBoard, WandB, MLflow adapters
- [Pyro Integration](https://sbi.readthedocs.io/en/latest/how_to_guide/23_using_pyro_with_sbi.html): Neural likelihoods in Pyro hierarchical models

## API Reference

- [Inference API](https://sbi.readthedocs.io/en/latest/api_reference.html): NPE, NLE, NRE, FMPE trainers and posteriors
- [Installation](https://sbi.readthedocs.io/en/latest/installation.html): pip install sbi, optional dependencies
- [Changelog](https://sbi.readthedocs.io/en/latest/changelog.html): Version history and migration notes
- [FAQ](https://sbi.readthedocs.io/en/latest/faq.html): Common issues (leakage, NaNs, pickling, unconstrained space)

## Optional

- [Hodgkin-Huxley Example](https://sbi.readthedocs.io/en/latest/tutorials/Example_00_HodgkinHuxleyModel.html): Neuroscience application
- [Decision-Making Model](https://sbi.readthedocs.io/en/latest/tutorials/Example_01_DecisionMakingModel.html): Cognitive science application
- [GPU Training](https://sbi.readthedocs.io/en/latest/how_to_guide/07_gpu_training.html): CUDA and MPS device configuration
- [Save and Load](https://sbi.readthedocs.io/en/latest/how_to_guide/07_save_and_load.html): Pickling posteriors, CPU_Unpickler
- [Resume Training](https://sbi.readthedocs.io/en/latest/how_to_guide/07_resume_training.html): Checkpoint and resume
- [MCMC Diagnostics](https://sbi.readthedocs.io/en/latest/how_to_guide/12_mcmc_diagnostics_with_arviz.html): ArviZ trace plots and R-hat
- [Conditional Distributions](https://sbi.readthedocs.io/en/latest/how_to_guide/05_conditional_distributions.html): Conditional pairplots
- [Importance Sampling](https://sbi.readthedocs.io/en/latest/how_to_guide/10_refine_posterior_with_importance_sampling.html): Refining posteriors
- [Permutation-Invariant Embeddings](https://sbi.readthedocs.io/en/latest/how_to_guide/08_permutation_invariant_embeddings.html): i.i.d. data with NPE
- [Contributing Guide](https://sbi.readthedocs.io/en/latest/contributing.html): How to contribute to sbi