User guide

bayesgm is a toolkit providing a AI-driven Bayesian generative modeling framework for various Bayesian inference tasks in complex, high-dimensional data.

The figure below illustrates the versatility of bayesgm, spanning dimensional reduction, data generation, Bayesian posterior inference, missing-data imputation, causal effect estimation, and counterfactual prediction:

bayesgm versatility

Which model should I use?

Use BGM family if your goal is:

  • conditional prediction/generation

  • missing-data imputation

  • dimension reduction

Use CausalBGM family if your goal is:

  • counterfactual prediction

  • ATE estimation

  • ITE estimation

Package overview

All models are installed from the same bayesgm package:

pip install bayesgm

Core namespaces:

  • bayesgm.models for model classes (BGM, CausalBGM, etc.)

  • bayesgm.datasets for built-in simulation/semi-synthetic samplers

  • bayesgm.utils for helpers and data IO

Next steps:

  1. Follow the Installation page in this section.

  2. Open the BGM or CausalBGM section in the sidebar.

  3. Start from the model quickstart block, then continue to tutorials.