Gammasl

Gammasl, short for Gamma-Stable Self-Learning, is a theoretical framework in data science and applied mathematics that combines gamma-stable stochastic processes with self-learning algorithms to model complex, non-Gaussian data. The approach emphasizes heavy tails, skewness, and non-stationarity, and is intended for streaming data and evolving systems.

In the model, latent variables are assumed to follow gamma distributions, which provides flexible dispersion and tail behavior. The observable variables are generated through a likelihood informed by these latent gamma variables. The inference procedure alternates between updating latent gamma variables and adjusting the self-learning component using online updates, variational methods, or Bayesian filtering.

History and development: The concept emerged in 2022 from a collaboration between researchers at the Institute for Advanced Computation and the Centre for Stochastic Modeling. It was proposed as a general-purpose tool for non-stationary environments and was described in several preprints.

Applications: Gammasl has been explored for time-series forecasting in finance, anomaly detection in sensor networks, and analysis of astrophysical gamma-ray data, where heavy-tailed noise and non-stationarity are common.

Implementation and status: As a research concept, there are experimental implementations in Python and R, but it is not a standard method and has not achieved widespread adoption. Public repositories include sample code and datasets to illustrate the learning process.

See also: gamma distribution, gamma process, self-learning, heavy-tailed distributions.