Nearest-Neighbor Gaussian Process

Cross-source consensus on Nearest-Neighbor Gaussian Process from 1 sources and 5 claims.

1 sources · 5 claims

How it works

The latent response function is given a nearest-neighbor Gaussian process prior. — Bayesian Sparsity Modeling of Shared Neural Response in Functional Magnetic Resonance Imaging Data
The GP variance is fixed at one so beta controls process variance and helps address nonidentifiability. — Bayesian Sparsity Modeling of Shared Neural Response in Functional Magnetic Resonance Imaging Data
The NNGP factorizes the joint density into conditional densities over nearest-neighbor sets. — Bayesian Sparsity Modeling of Shared Neural Response in Functional Magnetic Resonance Imaging Data
The NNGP is used because full Gaussian processes have roughly cubic covariance matrix costs. — Bayesian Sparsity Modeling of Shared Neural Response in Functional Magnetic Resonance Imaging Data
The response lengthscale is estimated by posterior updating after assigning a prior. — Bayesian Sparsity Modeling of Shared Neural Response in Functional Magnetic Resonance Imaging Data