thehardwarestore.ai

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probml/dynamax ★ 1019 118 APPLICATION

Dynamax is a JAX-native library for probabilistic state space models (SSMs), providing inference (filtering, smoothing, posterior sampling) and learning (EM, SGD, blocked Gibbs) for hidden Markov models (HMMs), linear Gaussian SSMs (LGSSMs), nonlinear Gaussian SSMs (NLGSSMs), generalized Gaussian SSMs (GGSSMs), and switching linear dynamical systems (SLDS). The architecture separates low-level pure functions (e.g., `hmm_filter`, `lgssm_filter`) from high-level object-oriented model classes (e.g., `GaussianHMM`, `LinearGaussianSSM`), all built on JAX for automatic differentiation, vectorization, and GPU/TPU acceleration. The library includes advanced algorithms such as parallel associative-scan filtering/smoothing, information-form Kalman filters, Rao-Blackwellized particle filters, and conditional-moments Gaussian filters (EKF, UKF, GHKF). It is designed for research use, with a focus on numerical correctness and performance, and is accompanied by extensive documentation, tutorials, and a JOSS publication.

RATING: 8.2/10 MAINT: active DRIFT: medium
#state-space-models #jax #probabilistic-inference #kalman-filter #hidden-markov-model #bayesian-machine-learning #time-series #research-library #hidden-markov-models #python
▸ 5 reusable components found
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