> ML_LITERATURE // GAUSS-1809-THEORIA-MOTUS-LEAST-SQUARES_v1.0
Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium
Carl Friedrich Gauss · Perthes et Besser, Hamburg (1809)
foundational1809foundationalthirdPartyReproduced
Principal Contribution
Connected least squares with probability theory via the normal (Gaussian) distribution and maximum likelihood estimation.
Operational Relevance
Establishes Gaussian noise assumptions and maximum likelihood foundations used in GLMs, Kalman filters, and regression loss functions.
Assumptions
- Measurement errors are independent and identically distributed according to a normal distribution
Limitations
- Gaussian error distribution fails under heavy-tailed real-world noise
Connected Algorithms, Architectures & Tools
Related Algorithms:
Related Architectures:
Implementing Libraries: