New calibration of Bayes: Exponent 0.5

Calculations made in remoton

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:
load(‘c:\hipertec\optimesto\bayes\resultados\info_Calibracionueva_predichoporbayes.mat’)
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…

>> load(‘c:\hipertec\optimesto\bayes\resultados\info_Calibracionueva_predichoporbayes_binbuenos.mat’)
>> prob=infoarchivos2prob(infoarchivos,[1 2 3]);
>> close all
>> plot(infoarchivos.pot,sum(sum(prob,2),3),’r’)
>> figure
>> imagesc(log10(infoarchivos.beta),log10(infoarchivos.alfa),squeeze(sum(prob)))
>> hold on
>> plot(-.9,-1.1,’w.’)

Good enough. If we have time after running controls for other values of alpha, beta and the exponent, we should run a few more controls in this situation to see the dispersion. Also, the lower sd observed in real data might be because local minima may give rise to very specific configurations. We might try with lower noise, and see what happens.

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