sbi

(★ 855)

sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has you covered.

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최종 버전 다운로드 (.zip)

- devcontainer.json
- Dockerfile
- - bug_report.yml
  - config.yml
  - feature_request.yml
- - release.md
- - build_codespace_image.yaml
  - cd.yml
  - ci.yml
  - docs-link-check.yml
  - latest-deps.yml
  - license-check.yml
  - lint.yml
  - manual_test.yml
  - publish.yml
  - refresh-test-durations.yml
  - update-uv-lock.yml
- codecov.yml
- dependabot.yml
- pull_request_template.md
- - custom.css
  - logo.svg
  - logo_bmbf.svg
- - 02_multiround_inference.ipynb
  - 03_density_estimators.ipynb
  - 04_embedding_networks.ipynb
  - 05_conditional_distributions.ipynb
  - 06_restriction_estimator.ipynb
  - 07_sensitivity_analysis.ipynb
  - 10_diagnostics_posterior_predictive_checks.ipynb
  - 11_diagnostics_simulation_based_calibration.ipynb
  - 12_iid_data_and_permutation_invariant_embeddings.ipynb
  - 13_diagnostics_lc2st.ipynb
  - 15_importance_sampled_posteriors.ipynb
  - 17_plotting_functionality.ipynb
  - 18_training_interface.ipynb
  - 19_vector_field_methods.ipynb
  - 21_diagnostics_misspecification_checks.ipynb
  - diagnostics.rst
  - neural_nets.rst
  - prior_and_simulator.rst
  - sampling.rst
  - toy_posterior_for_07_cc.py
  - training.rst
  - utils_13_diagnosis_sbc.py
  - visualization.rst
- - analysis.rst
  - diagnostics.rst
  - embedding_nets.rst
  - estimators.rst
  - neural_nets.rst
  - posterior_parameters.rst
  - posteriors.rst
  - potentials.rst
  - prior_and_simulator.rst
  - training.rst
  - utilities.rst
- - question_01_leakage.md
  - question_02_nans.md
  - question_03_pickling_error.md
  - question_04_unconstrained.md
- - 00_custom_prior.ipynb
  - 01_crafting_summary_statistics.ipynb
  - 02_multiround_inference.ipynb
  - 03_choose_neural_net.ipynb
  - 03_density_estimators.ipynb
  - 04_embedding_networks.ipynb
  - 05_conditional_distributions.ipynb
  - 06_choosing_inference_method.ipynb
  - 07_gpu_training.ipynb
  - 07_resume_training.ipynb
  - 07_save_and_load.ipynb
  - 08_permutation_invariant_embeddings.ipynb
  - 09_sampler_interface.ipynb
  - 10_refine_posterior_with_importance_sampling.ipynb
  - 11_iid_sampling_with_nle_or_nre.ipynb
  - 12_mcmc_diagnostics_with_arviz.ipynb
  - 13_diagnostics_lc2st.ipynb
  - 14_choose_diagnostic_tool.ipynb
  - 15_expected_coverage.ipynb
  - 16_sbc.ipynb
  - 17_tarp.ipynb
  - 18_model_misspecification.ipynb
  - 19_posterior_parameters.ipynb
  - 20_time_series_embedding.ipynb
  - 21_hyperparameter_tuning.ipynb
  - 22_experiment_tracking.ipynb
  - 23_using_pyro_with_sbi.ipynb
  - 24_abstraction_levels.ipynb
  - 25_choosing_vector_field_options.ipynb
  - 26_variational_inference.ipynb
  - diagnostics.rst
  - neural_nets.rst
  - prior_and_simulator.rst
  - sampling.rst
  - training.rst
  - visualization.rst
- - ep-00-process.md
  - ep-01-pluggable-training.md
- - 00_getting_started.ipynb
  - 01_Bayesian_workflow.ipynb
  - 16_implemented_methods.ipynb
  - Example_00_HodgkinHuxleyModel.ipynb
  - Example_01_DecisionMakingModel.ipynb
  - example_01_utils.py
  - HH_helper_functions.py
- advanced_tutorials.rst
- api_reference.rst
- applications.rst
- changelog.md
- code_of_conduct.md
- conf.py
- contributing.md
- contributor_guide.rst
- credits.md
- examples.rst
- faq.rst
- goal.png
- how_to_guide.rst
- index.rst
- installation.md
- llms.txt
- logo.png
- make.bat
- Makefile
- proposals.md
- README.md
- tutorials.rst
- - __init__.py
  - conditional_density.py
  - plot.py
  - plotting_classes.py
  - sensitivity_analysis.py
  - tensorboard_output.py
- - __init__.py
  - lc2st.py
  - misspecification.py
  - sbc.py
  - tarp.py
- - __init__.py
  - minimal.py
- - __init__.py
- - __init__.py
  - build_context.py
  - factory.py
  - ratio_estimators.py
- - __init__.py
- - __init__.py
  - gaussian_mixture.py
  - linear_gaussian.py
  - simutils.py
- - __init__.py
  - analysis_utils.py
  - conditional_density_utils.py
  - diagnostics_utils.py
  - io.py
  - kde.py
  - metrics.py
  - nn_utils.py
  - plotting_helpers.py
  - potentialutils.py
  - pyroutils.py
  - restriction_estimator.py
  - sbiutils.py
  - simulation_utils.py
  - torchutils.py
  - tracking.py
  - typechecks.py
  - user_input_checks.py
  - user_input_checks_utils.py
  - vector_field_utils.py
- __init__.py
- __version__.py
- sbi_types.py
- - __init__.py
  - base_task.py
  - gaussian_linear.py
  - linear_mvg.py
  - slcp.py
  - two_moons.py
- __init__.py
- abc_test.py
- analysis_test.py
- base_test.py
- bm_test.py
- build_context_test.py
- circular_import_test.py
- compose_standardization_test.py
- conftest.py
- density_estimator_builder_test.py
- density_estimator_test.py
- embedding_net_test.py
- ensemble_test.py
- factory_config_test.py
- inference_on_device_test.py
- inference_with_NaN_simulator_test.py
- lc2st_test.py
- linearGaussian_mdn_test.py
- linearGaussian_simulator_test.py
- linearGaussian_snle_test.py
- linearGaussian_snpe_test.py
- linearGaussian_snre_test.py
- linearGaussian_vector_field_test.py
- marginal_builder_integration_test.py
- marginal_estimator_test.py
- mcmc_test.py
- metrics_test.py
- misspecification_test.py
- mixture_density_estimator_test.py
- mnle_test.py
- mnpe_test.py
- mog_test.py
- multiprocessing_test.py
- npe_nle_builder_integration_test.py
- nre_builder_integration_test.py
- plot_test.py
- posterior_nn_test.py
- posterior_parameters_test.py
- posterior_sampler_test.py
- potential_test.py
- prior_device_test.py
- pyroutils_test.py
- ratio_estimator_test.py
- rejection_sampling_test.py
- save_and_load_test.py
- sbc_test.py
- sbiutils_test.py
- scale_equivariance_test.py
- score_samplers_test.py
- sensitivity_analysis_test.py
- simulator_utils_test.py
- strip_notebook_outputs.py
- tarp_test.py
- test_utils.py
- torchutils_test.py
- training_dataclasses_test.py
- transforms_test.py
- tutorials_test.py
- user_input_checks_test.py
- vector_field_nets_test.py
- vf_builder_integration_test.py
- vf_estimator_test.py
- vi_test.py
.gitattributes
.gitignore
.pre-commit-config.yaml
.readthedocs.yaml
.test_durations
AGENTS.md
CHANGELOG.md
CITATION.cff
CLAUDE.md
CODE_OF_CONDUCT.md
codemeta.json
CONTRIBUTING.md
LICENSE.txt
MANIFEST.in
pyproject.toml
README.md
SECURITY.md
uv.lock

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/sbi-dev/sbi

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd sbi

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

uv pip install sbi

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

uv pip install "sbi[pyro]" # for Pyro samplers (HMC, NUTS)

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

uv pip install "sbi[pymc]" # for PyMC samplers (HMC, NUTS, Slice)

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

uv pip install "sbi[all]" # both Pyro and PyMC

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

sbi

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# 설치 가이드

2. Python

오늘의 추천 프로젝트

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sbi

파일 탐색기

# 설치 가이드

2. Python

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

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