Output: Hierarchical Model assuming common mortality

 

Posterior means, posterior standard deviations and 95% credible intervals (Inference->Samples->stats)

 

image007.png

Sample path (history)

 

image038.pngimage040.png

 

 

Posterior probability densities (Inference->Samples->density)

(Further, right click on the figure->Margins-> Specialc->Smooth -> change from 0.2 to 0.1-> apply all)

 

 

image042.pngimage044.png

Sample autocorrelation function (Inference->Samples->auto corr)

image046.pngimage048.png

 

 

Running quantile plot (Inference->Samples->quantiles)

 

image050.pngimage052.png

 

Posterior predictive analysis. pleft:Pr(ypred[i] =< y[i]). pright: Pr(y.pred[i]>=y[i])

Inference-> Compare-> node:pleft and click on caterpillar)

(Further, right-click on the figure, Propertyc-> Margins -> Specialc-> check grankh and showbaseline:0.05)

image003.png

*P(ypred[85]<y[85])=0.0056 < 0.05

*P(ypred[63]<y[63])=0.0062 < 0.05

 

Inference-> Compare-> node:pright and click on caterpillar)

(Further, right-click on the figure, Propertyc-> Margins -> Specialc-> check grankh and showbaseline:0.05)

image005.png

*P(ypred[68]>y[68])=0.0196 < 0.05

*P(ypred[9] > y[9] ) =0.0396 < 0.05

*P(ypred[93]>y[93])=0.0470 < 0.05