Linear and probabilistic models · MLD-0454
Gaussian process
A distribution over functions specified by mean and covariance functions.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Predict a smooth function with uncertainty.
Original caveat
Exact inference can become expensive as sample count grows.
Context and aliases
Level: Intermediate. Stages: Training · Inference.
Tags: ML.
Aliases: GP.
Related concepts
Concepts linking here
Technology connections
- DGL — Framework for graph machine learning.
- Diffrax — Differential equation solving library for JAX.
- DoWhy — Causal inference framework.
- EconML — Machine-learning methods for heterogeneous treatment effects.
- GPyTorch — Gaussian-process modeling library in PyTorch.
- NetworkX — Python library for graph analysis.
- NumPyro — JAX-based probabilistic programming library.
- PyMC — Probabilistic programming library for Bayesian models.
- PyTorch Geometric — Graph neural-network library built on PyTorch.
Original references
- scikit-learn: Gaussian processes ↗ · Reference link
- scikit-learn: linear models ↗ · Reference link
- scikit-learn: naive Bayes ↗ · Reference link
- Original reference URL ↗