## doc: bfgs_search_prior

Optimize prior hyper-parameter with respect to the pseudo-likelihood, using the BFGS algorithm

### Syntax

HP = bfgs_search_prior(Xt, Yt, HPini, kfun, basis)

### Arguments

*Xt* matrix *(n, d)* where *n* is the number of data points and *d* is the dimension*Yt* vector *(n, 1)* of noisy observations*HPini* vector *(h, 1)* of initial hyper-parameters formatted as : *[log(sf2) log(sn) log(w1) log(w2)]**kfun* kernel function such as *kernel_se**basis* basis function such as *basis_none*

### Outputs

*HP* vector *(h, 1)* of locally optimal log hyper-parameters*kernel* found kernel function*noise* found noise standard deviation

### See also

gp_loolik