Bayesflow

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BayesFlow is a Python library for efficient Bayesian modeling with deep learning. It provides a user-friendly API for amortized Bayesian workflows and supports multiple backends like PyTorch, TensorFlow, and JAX.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is bayesflow?

BayesFlow is a powerful Python library designed for efficient Bayesian inference using deep learning techniques. It offers a user-friendly API that streamlines amortized Bayesian workflows, making complex modeling tasks more accessible. The library provides a rich collection of generative models, including diffusion and consistency models, and boasts multi-backend support via Keras3, allowing users to leverage PyTorch, TensorFlow, or JAX. It is suitable for both complex simulators and traditional statistical models, offering a streamlined workflow for parameter estimation, model comparison, and validation, especially when conventional methods are inefficient or unavailable.

Best used for

Ideal for data scientists and developers who need to perform efficient Bayesian inference, build generative models, and analyze complex data. Especially valuable for researchers and students working with simulation-based inference or traditional statistical models requiring deep learning approaches.

Common actions

perform Bayesian inference

build deep learning models

analyze complex data

simulate statistical models

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Capabilities

Key features

Amortized Bayesian workflows
Generative models collection
Multi-backend support
Parameter estimation
Model comparison
Model validation

Target Audience

data scientistdeveloperprofessorstudent

Integrations

pytorchtensorflowjax

Pricing & Plans

Open Source

Free

FAQs

Which backend is recommended for BayesFlow?

JAX is currently the recommended backend for BayesFlow as it offers the fastest performance. However, you can also use PyTorch or TensorFlow depending on your preference and existing setup.

How do I install BayesFlow?

You can install the latest stable version of BayesFlow from PyPI using `pip install "bayesflow>=2.0"`. You will also need to install one of the supported machine learning backends like JAX, PyTorch, or TensorFlow.

What is the main difference between BayesFlow 2 and previous versions?

BayesFlow 2.0+ is a complete rewrite with improved modularity and extensibility. The key difference is its multi-backend support via Keras3, whereas previous versions were exclusively based on TensorFlow.

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