Documentation

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

The framework is built upon Bayesian principles combined with modern AI models, enabling flexible modeling of complex dependencies with principled uncertainty estimation.

Currently, the bayesgm package includes two model families:

• BGM: Bayesian generative modeling for arbitrary conditional inference (foundational model).

• CausalBGM: Bayesian generative modeling for causal effect estimation.

Getting Started

• User guide
• Installation

Model Docs

• BGM - A Bayesian Generative Modeling Approach for Arbitrary Conditional Inference
  • BGM applicability
  • Method highlights
  • Quickstart
  • Main reference
  • Tutorials
• CausalBGM - An AI-powered Bayesian generative modeling approach for causal inference in observational studies
  • CausalBGM applicability
  • Method highlights
  • Quickstart
  • Main references
  • Tutorials

API Reference

• API Reference
  • Models
  • Datasets
  • Utilities

References

• References