State Space Model Programming in Turing.jl
Tim Hargreaves, Qing Li, Charles Knipp, Frederic Wantiez, Simon J. Godsill, Hong Ge

TL;DR
This paper introduces Julia packages SSMProblems.jl and GeneralisedFilters.jl within Turing.jl, providing a scalable, composable framework for defining and performing inference on state space models, enhancing flexibility and efficiency.
Contribution
It presents a unified, modular framework for state space models in Julia, enabling flexible inference and scalability with GPU support, addressing limitations of existing tools.
Findings
Supports multiple inference algorithms including Kalman and particle filtering
Enhances scalability with memory management and GPU acceleration
Facilitates easy definition and experimentation with SSMs
Abstract
State space models (SSMs) are a powerful and widely-used class of probabilistic models for analysing time-series data across various fields, from econometrics to robotics. Despite their prevalence, existing software frameworks for SSMs often lack compositionality and scalability, hindering experimentation and making it difficult to leverage advanced inference techniques. This paper introduces SSMProblems.jl and GeneralisedFilters.jl, two Julia packages within the Turing.jl ecosystem, that address this challenge by providing a consistent, composable, and general framework for defining SSMs and performing inference on them. This unified interface allows researchers to easily define a wide range of SSMs and apply various inference algorithms, including Kalman filtering, particle filtering, and combinations thereof. By promoting code reuse and modularity, our packages reduce development…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Bayesian Modeling and Causal Inference · Formal Methods in Verification
