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New library Dynestyx simplifies state-space models for machine learning

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Dynestyx, a new probabilistic programming library designed to simplify the integration of state-space models (SSMs) into modern probabilistic programming languages. This library aims to make advanced methods for dynamical systems more accessible to practitioners by providing a unified interface for specifying priors, performing inference on mixed-effect data, and quantifying uncertainty in state and parameter estimates. Dynestyx is intended to streamline the Bayesian workflow for applications in statistics, signal processing, and machine learning. AI

IMPACT Simplifies advanced Bayesian inference for dynamical systems, potentially accelerating research and application in machine learning.

RANK_REASON The cluster contains an academic paper detailing a new software library for machine learning research.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New library Dynestyx simplifies state-space models for machine learning

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Daniel Waxman, Dmitry Batenkov, John Feser, Andy Zane, Eli Bingham, Youssef Marzouk, Matthew E. Levine ·

    Dynestyx: A Probabilistic Programming Library for Dynamical Systems

    arXiv:2606.16985v1 Announce Type: cross Abstract: State-space models (SSMs) are the standard formalism for Bayesian treatment of dynamical systems, with natural applications in statistics, signal processing, and machine learning. Despite their importance in both theory and applic…

  2. arXiv stat.ML TIER_1 English(EN) · Matthew E. Levine ·

    Dynestyx: A Probabilistic Programming Library for Dynamical Systems

    State-space models (SSMs) are the standard formalism for Bayesian treatment of dynamical systems, with natural applications in statistics, signal processing, and machine learning. Despite their importance in both theory and application, dynamical systems have proven difficult to …

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