# GP pred_rng doc language

Quick question.

So in the docs we have:

vector gp_pred_rng(real[] x2,
vector y1,
real[] x1,
real alpha,
real rho,
real sigma,
real delta) {
int N1 = rows(y1);
int N2 = size(x2);
vector[N2] f2;
{
matrix[N1, N1] L_K;
vector[N1] K_div_y1;
matrix[N1, N2] k_x1_x2;
matrix[N1, N2] v_pred;
vector[N2] f2_mu;
matrix[N2, N2] cov_f2;
matrix[N2, N2] diag_delta;
matrix[N1, N1] K;
for (n in 1:N1)
K[n, n] = K[n,n] + square(sigma);
L_K = cholesky_decompose(K);
K_div_y1 = mdivide_left_tri_low(L_K, y1);
K_div_y1 = mdivide_right_tri_low(K_div_y1', L_K)';
k_x1_x2 = gp_exp_quad_cov(x1, x2, alpha, rho);
f2_mu = (k_x1_x2' * K_div_y1);
v_pred = mdivide_left_tri_low(L_K, k_x1_x2);
cov_f2 = gp_exp_quad_cov(x2, alpha, rho) - v_pred' * v_pred;
diag_delta = diag_matrix(rep_vector(delta, N2));

f2 = multi_normal_rng(f2_mu, cov_f2 + diag_delta);
}
return f2;
}


Which is cool, but any objections to changing x_1, x_2, which could be mistaken for elements of a vector x or column vectors, to something like x and x_{pred} (x, x_pred)? Or to match the language in the docs, and what’s commonly used for predictive inference, just x and \tilde{x} (x and x_tilde)?

Usually people don’t object to this kind of cosmetic change in code. This one in particular sounds like a good idea. I don’t have an opinion on _pred (don’t know what it means) vs. _tilde

That sounds like a good idea. I don’t have any idea what the _pred is for, but I’m not a GP expert.

But why is that using real arrays rather than vectors? I’d have thought everything would be coded in vectors.

Good observation, that’s a downvote then. It’s supposed to mean predict.

Similar to our pdfs/pmfs’s response, many are not templated support inputs of column vectors or column vectors of column vectors. So that’s the reason. It was for ease of implementation.

This will be changed eventaully.