## New calibration of Bayes: Exponent 0.5

I explore different values of the noise:

ruidos2=[.01 .05 .1 .5 1 5];
matlabpool open local 4
parfor (c=1:length(ruidos2))
[pos{c},coste_hist{c},error_hist{c}]=simulaelegans_saltopeque_ruidonorm(todas.A*10^-1.1,todas.M*10^-.9+todas.S,todas.f,.5,.001,ruidos2(c),5*10^6,0);
end
pos_opt=NaN(279,100)

parfor (c=1:100)

pos_opt(:,c)=coste2pos_num_ruido(todas.A*10^-1.1,todas.M*10^-.9+todas.S,todas.f,.5,0);

end
for c=1:100

costes(c)=pos2coste(todas.A*10^-1.1,todas.M*10^-.9+todas.S,todas.f,pos_opt(:,c),.5);

end
[m,ind]=min(costes);
for c=1:length(pos)

errores(c)=mean(abs(pos{c}-pos_reopt));

end

I would say that below 0.5 the system gets stuck in global minima, and above 0.5 the standard deviation increases due to the noise. I take the point with noise 0.5, which has a convenient mean error.

I run Bayes:

infoarchivos=Bayes_alfabeta(todas.A,todas.M,todas.S,todas.f,pos{4},0:.02:1,0:.25:4,10.^(-2:.3:1),10.^(-2:.3:1),10.^(-2:6/6:4),10.^(-11:26/6:15),[],[2 2],[10 10],’Calibracionueva_predichoporbayes’,[],4,1);

On my desktop in the lab:
prob=infoarchivos2prob(infoarchivos,[1 2 3]);
>> plot(infoarchivos.pot,sum(sum(prob,2),3),’r’)
>> imagesc(log10(infoarchivos.beta),log10(infoarchivos.alfa),squeeze(sum(prob)))
>> close all
>> imagesc(log10(infoarchivos.beta),log10(infoarchivos.alfa),squeeze(sum(prob)))
>> hold on
>> plot(-.9,-1.1,’w.’)

This second figure seemed different when plotted in remotón. There was a long diagonal towards the right-bottom corner. But the maximum was in the same place, and the right value was inside the high probability area.

Anyway, works fine. I run it with a binning that includes the exact values for alfa and beta…