Module sparse_gp 

Sparse Gaussian Process implementations with comprehensive approximation methods

This module provides a complete sparse GP framework with multiple approximation methods, SIMD acceleration, scalable inference, and structured kernel interpolation.

Re-exports

pub use approximations::InducingPointSelector;

pub use approximations::SparseApproximationMethods;

pub use inference::LanczosMethod;

pub use inference::PreconditionedCG;

pub use inference::ScalableInference;

pub use kernels::KernelOps;

pub use kernels::MaternKernel;

pub use kernels::RBFKernel;

pub use kernels::SparseKernel;

pub use simd_operations::simd_sparse_gp;

pub use ski::FittedTensorSKI;

pub use ski::TensorSKI;

pub use variational::StochasticVariationalInference;

pub use variational::VariationalFreeEnergy;

pub use core::*;

Modules

approximations

Sparse approximation methods for Gaussian Processes

core

Core types, enums, and structures for sparse Gaussian Process implementation

inference

Scalable inference methods for sparse Gaussian Processes

kernels

Kernel functions and matrix operations for sparse Gaussian Processes

simd_operations

SIMD-accelerated operations for sparse Gaussian Processes

ski

Structured Kernel Interpolation (SKI/KISS-GP) for fast GP inference

utils

Utility functions and helpers

variational

Variational methods for sparse Gaussian Process approximation