>> clear
>> load datos_celegans_struct
>> [pos,omega_general,costes,x]=coste2pos_restofijas(todas.A*.05,todas.M*1.5+todas.S,todas.f,todas.pos_real,1);
>> desv=abs(todas.pos_real-pos);
>> plot(desv,omega_general,’.’)
To start with, only one outlier. Note that the new omega depends on the exponent, so this by itself favors exponent 1.
Bad news for the fit to the mean (without the outlier):
>> buenas=(desv<.2 | omega_general<20);
>> sum(~buenas)
ans =
1
>> [xi,b]=omegadesv2bestfittingexponent_exp(desv(buenas),omega_general(buenas))
xi =
4.5068
b =
0.1092
And of course, again great news for the fit to the envelope (again, without the outlier):
>> [desv_acum,omega_media]=omegadesv_acumuladas(desv(buenas),omega_general(buenas),1,4);
>> plot(log10(desv_acum),log10(omega_media),’.-‘)
I am starting to believe the theory about Bayes being especially affected by the most deviated ones, and the envelope getting that.
Cuaderno de laboratorio de al. 
14 August 2009 at 9:33 pm
Good theory that one…:)
Question:Is there something special about the prob we get from bayes that makes the mean particularly sensitive to amount of data orparticularly insensitive to omega?
14 August 2009 at 10:04 pm
Si tuvieses tiempo: ver como cambia la media y la envolvente con el numero de pasos en simulated annealing.
Sepuede pintar el valor del exponenente del fit en el tiempo, paramedia y envolvente.