AaltoML/kalman-jax
Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX
This project helps machine learning researchers and data scientists analyze sequential data where observations are related over time. It takes in time-series data or other temporally linked datasets and provides refined insights into underlying trends and predictions, even when the relationships are complex. It's designed for those working with probabilistic models who need efficient and accurate inference.
103 stars. No commits in the last 6 months.
Use this if you are developing or applying advanced machine learning models to analyze time-series data and need efficient, robust methods for tracking and predicting dynamic systems.
Not ideal if you are looking for a plug-and-play solution for standard regression or classification tasks without a specific focus on temporal dependencies or probabilistic modeling.
Stars
103
Forks
13
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Jul 06, 2023
Commits (30d)
0
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