Created
May 27, 2016 17:50
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stan code for sampling response via Gaussian process and squared exponential kernel. See: https://github.com/stan-dev/example-models/tree/master/misc/gaussian-process
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// Predict from Gaussian Process | |
// All data parameters must be passed as a list to the Stan call | |
// Based on original file from https://code.google.com/p/stan/source/browse/src/models/misc/gaussian-process/ | |
data { | |
int<lower=1> N1; | |
vector[N1] x1; | |
vector[N1] y1; | |
int<lower=1> N2; | |
vector[N2] x2; | |
real sigma_sq; | |
real eta_sq; | |
real rho_sq; | |
} | |
transformed data { | |
int<lower=1> N; | |
vector[N1+N2] x; | |
vector[N1+N2] mu; | |
cov_matrix[N1+N2] Sigma; | |
N <- N1 + N2; | |
for (n in 1:N1) x[n] <- x1[n]; | |
for (n in 1:N2) x[N1 + n] <- x2[n]; | |
for (i in 1:N) mu[i] <- 0; | |
for (i in 1:N) | |
for (j in 1:N) | |
//// RBF, aka squared exp kernel, aka Gaussian kernel | |
Sigma[i,j] <- eta_sq*exp(-rho_sq*pow(x[i] - x[j],2)) + | |
if_else(i==j, sigma_sq, 0.0); | |
} | |
parameters { | |
vector[N2] y2; | |
} | |
model { | |
vector[N] y; | |
for (n in 1:N1) y[n] <- y1[n]; | |
for (n in 1:N2) y[N1 + n] <- y2[n]; | |
y ~ multi_normal(mu,Sigma); | |
} |
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