Dynamax
A Python library for probabilistic state space modeling and inference, built on JAX.
What is Dynamax?
Dynamax is a Python library for probabilistic state space modeling, providing tools for inference and parameter estimation in models like Hidden Markov Models and Linear Dynamical Systems. It is built on JAX, enabling high-performance computations with GPU support and automatic differentiation. The library addresses the need for efficient, scalable algorithms for sequential data analysis in machine learning and statistics.
Researchers and practitioners in machine learning, statistics, and time-series analysis who need to model sequential data with state space models. It is particularly suited for those already using the JAX ecosystem for differentiable programming and high-performance computing.
Developers choose Dynamax for its JAX-native implementation, which offers performance benefits like GPU acceleration and easy integration with other JAX libraries. Its support for a wide range of SSM types and dual API (low-level and object-oriented) provides flexibility for both advanced customization and user-friendly modeling.
Overview
A Python package for probabilistic state space modeling with JAX
Use Cases
Best For
- Inference and learning in Hidden Markov Models with Gaussian or non-Gaussian emissions
- Time-series forecasting using Linear Gaussian State Space Models
- Bayesian parameter estimation for state space models with MCMC or SMC
- Sequential data analysis in research requiring GPU-accelerated computations
- Building custom probabilistic models for sequential data within the JAX ecosystem
- Educational purposes for understanding state space model algorithms and implementations
Not Ideal For
- Projects requiring deep integration with PyTorch or TensorFlow ecosystems for non-JAX workflows
- Applications focused on non-sequential data or non-probabilistic models like computer vision or NLP transformers
- Teams needing out-of-the-box commercial support, stable APIs, or drag-and-drop interfaces for production deployments
Pros & Cons
Pros
Leverages JAX for GPU acceleration, automatic differentiation, and efficient vectorization with vmap, enabling fast, scalable inference and learning for large datasets.
Supports a wide range of state space models including HMMs, Linear and Nonlinear Gaussian SSMs, and generalized models with non-Gaussian emissions, as shown in the example code for Gaussian HMMs.
Offers both user-friendly object-oriented model classes for quick prototyping and low-level, functionally pure algorithms for advanced customization and composability.
Seamlessly integrates with JAX libraries like optax for stochastic gradient descent and Blackjax for Bayesian inference, enhancing parameter estimation capabilities without extra overhead.
Cons
Relies entirely on JAX, which has a steeper learning curve due to functional programming paradigms and less community support compared to frameworks like PyTorch, limiting accessibility for some developers.
Exclusively targets state space models, so it lacks support for other probabilistic models like Bayesian networks or deep generative models, requiring additional libraries for broader tasks.
As a research-oriented library, it may have less frequent updates, potential breaking changes, and sparse documentation for edge cases compared to commercially backed alternatives.
Frequently Asked Questions
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