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fix: griffe warnings about .md links; refactoring text.
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docs/docs/contribute.md

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When you create a PR onto `main`, our Continuous Integration (CI) actions on
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GitHub will perform the following checks:
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- **`ruff`** for linting and formatting (including `black`, `isort`, and `flake8`)
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- **[`ruff`](https://docs.astral.sh/ruff/formatter/)** for linting and formatting
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(including `black`, `isort`, and `flake8`)
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- **[`pyright`](https://github.com/Microsoft/pyright)** for static type checking.
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- **`pytest`** for running a subset of fast tests from our test suite.
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- **[`pytest`](https://docs.pytest.org/en/stable/index.html)** for running a subset of
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fast tests from our test suite.
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If any of these fail, try reproducing and solving the error locally:
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- **`ruff`**: Make sure you have `pre-commit` installed locally with the same
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version as specified in the [requirements](pyproject.toml). Execute it
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using `pre-commit run --all-files`. `ruff` tends to give informative error
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messages that help you fix the problem. Note that pre-commit only detects
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problems with `ruff` linting and formatting, but does not fix them. You can
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fix them either by running `ruff check . --fix(linting)`, followed by
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`ruff format . --fix(formatting)`, or by hand.
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- **`ruff`**: Make sure you have `pre-commit` installed locally with the same version as
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specified in the
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[`pyproject.toml`](https://github.com/sbi-dev/sbi/blob/main/pyproject.toml). Execute it
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using `pre-commit run --all-files`. `ruff` tends to give informative error messages
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that help you fix the problem. Note that pre-commit only detects problems with `ruff`
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linting and formatting, but does not fix them. You can fix them either by running
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`ruff check . --fix(linting)`, followed by `ruff format . --fix(formatting)`, or by
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hand.
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- **`pyright`**: Run it locally using `pyright sbi/` and ensure you are using
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the same
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`pyright` version as used in the CI (which is the case if you have installed

docs/docs/faq.md

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5. [How should I save and load objects in `sbi`?](faq/question_05_pickling.md)
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6. [Can I stop neural network training and resume it later?](faq/question_06_resume_training.md)
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7. [How can I use a prior that is not defined in PyTorch?](faq/question_07_custom_prior.md)
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See also [discussion page](https://github.com/sbi-dev/sbi/discussions) and [issue
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tracker](https://github.com/sbi-dev/sbi/issues) on the `sbi` GitHub repository for
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recent questions and problems.

docs/docs/index.md

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- :rocket: [__Tutorials and Examples__](tutorials/index.md)
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<br/><br/>
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*Various examples illustrating how to<br/> [get
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started](tutorials/00_getting_started_flexible/) or use the `sbi` package.*
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started](tutorials/00_getting_started_flexible.md) or use the `sbi` package.*
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- :building_construction: [__Reference API__](reference/index.md)
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<br/><br/>
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Below, we list all implemented methods and the corresponding publications. To see
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how to access these methods in `sbi`, check out our [Inference API's reference](
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reference/inference.md) and the [tutorial on implemented
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methods](tutorials/16_implemented_methods.ipynb).
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methods](tutorials/16_implemented_methods.md).
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### Posterior estimation (`(S)NPE`)
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docs/docs/tutorials/index.md

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## Introduction
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<div class="grid cards" markdown>
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- [Getting started](00_getting_started_flexible)
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- [Amortized inference](01_gaussian_amortized)
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- [Implemented algorithms](16_implemented_methods)
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- [Getting started](00_getting_started_flexible.md)
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- [Amortized inference](01_gaussian_amortized.md)
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- [Implemented algorithms](16_implemented_methods.md)
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</div>
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## Advanced
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<div class="grid cards" markdown>
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- [Multi-round inference](03_multiround_inference)
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- [Sampling algorithms in sbi](11_sampler_interface)
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- [Custom density estimators](04_density_estimators)
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- [Embedding nets for observations](05_embedding_net)
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- [SBI with trial-based data](14_iid_data_and_permutation_invariant_embeddings)
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- [Handling invalid simulations](08_restriction_estimator)
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- [Crafting summary statistics](10_crafting_summary_statistics)
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- [Multi-round inference](03_multiround_inference.md)
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- [Sampling algorithms in sbi](11_sampler_interface.md)
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- [Custom density estimators](04_density_estimators.md)
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- [Embedding nets for observations](05_embedding_net.md)
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- [SBI with trial-based data](14_iid_data_and_permutation_invariant_embeddings.md)
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- [Handling invalid simulations](08_restriction_estimator.md)
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- [Crafting summary statistics](10_crafting_summary_statistics.md)
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- [Importance sampling posteriors](17_importance_sampled_posteriors.md)
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</div>
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## Diagnostics
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<div class="grid cards" markdown>
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- [Posterior predictive checks](12_diagnostics_posterior_predictive_check)
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- [Simulation-based calibration](13_diagnostics_simulation_based_calibration)
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- [Density plots and MCMC diagnostics with ArviZ](15_mcmc_diagnostics_with_arviz)
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- [Posterior predictive checks](12_diagnostics_posterior_predictive_check.md)
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- [Simulation-based calibration](13_diagnostics_simulation_based_calibration.md)
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- [Density plots and MCMC diagnostics with ArviZ](15_mcmc_diagnostics_with_arviz.md)
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- [Local-C2ST coverage checks](18_diagnostics_lc2st.md)
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</div>
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## Analysis
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<div class="grid cards" markdown>
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- [Conditional distributions](07_conditional_distributions)
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- [Posterior sensitivity analysis](09_sensitivity_analysis)
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- [Conditional distributions](07_conditional_distributions.md)
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- [Posterior sensitivity analysis](09_sensitivity_analysis.md)
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- [Plotting functionality](19_plotting_functionality.md)
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</div>
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## Examples
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<div class="grid cards" markdown>
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- [Hodgkin-Huxley model](../examples/00_HH_simulator)
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- [Decision-making model](../examples/01_decision_making_model)
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- [Hodgkin-Huxley model](../examples/00_HH_simulator.md)
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- [Decision-making model](../examples/01_decision_making_model.md)
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</div>

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