GP with Gaussian noise model

gpcf = 

           type: 'gpcf_sexp'
    lengthScale: [1.1000 1.2000]
     magnSigma2: 0.0400
              p: [1x1 struct]
             fh: [1x1 struct]


K =

    0.0400    0.0187    0.0019
    0.0187    0.0400    0.0187
    0.0019    0.0187    0.0400


C =

    0.0800    0.0187    0.0019
    0.0187    0.0800    0.0187
    0.0019    0.0187    0.0800

 MAP estimate for the parameters
 TolFun reached. Func-count 33. Final f(x)=41.4902. Elapsed time 0.58
    'log(sexp.magnSigma2)'
    'log(sexp.lengthScale x 2)'
    'log(gaussian.sigma2)'

    3.5983    0.8838    0.8323    0.0428

 Grid integration over the parameters
 IA-grid: finding the mode
 IA-grid: computing Hessian using multiplication
 IA-grid: evaluating density in a grid
 IA-grid: evaluated density at 341 points
    Elapsed time 12.01 seconds
 IA-grid: Total elapsed time 20.93 seconds
 MCMC integration over the parameters
 Using SLS sampler for hyperparameters
 cycle  etr      slsrej  
   20  44.827  sls  
   40  42.705  sls  
   60  42.124  sls  
   80  44.214  sls  
  100  42.966  sls  
  120  43.217  sls  
  140  41.865  sls  
  160  44.764  sls  
  180  42.241  sls  
  200  41.686  sls  
  220  42.983  sls  
Done

 gp hyperparameters: 
 
    1.2805   -0.1235   -0.1836   -3.1521

Demo completed in 1.001 minutes
