What is SLT?#
Singular Learning Theory (SLT) is a mathematical framework developed by Sumio Watanabe for analyzing statistical learning when the model is singular (non-regular).
Key Concepts#
- RLCT (Real Log Canonical Threshold): A key quantity that determines generalization error
- Free Energy: Measures model complexity in Bayesian inference
- Singular Models: Models where the Fisher information matrix is degenerate
References#
- Watanabe, S. (2009). Algebraic Geometry and Statistical Learning Theory
- Watanabe, S. (2018). Mathematical Theory of Bayesian Statistics