## doc: gp_pred

Posterior mean and variance of GP given the kernel matrices and the Bayesian inferance

### Syntax

[mu, sigma2] = gp_pred(Kts, dKss, BayesInv)
[mu, sigma2] = gp_pred(Kts, dKss, BayesInv, Ht, Hs)

### Arguments

*Kts* matrix *(nt, ns)* of kernel between the points of *Xt* and *Xs**dKss* matrix *(ns, 1)* of diagonal kernel between the points of *Xs**BayesInv* structure array returned by *gp_inf(Ht, Ktt, Yt, noise)**Ht* matrix *(nt, b)* of basis data for *Xt**Hs* matrix *(ns, b)* of basis data for *Xs*

### Outputs

*mu* matrix *(ns, 1)* of posterior mean *sigma2* matrix *(ns, 1)* of posterior variance

### See also

gp_inf | gp_dist