Inverse Weibull model



   [inv_weibull_ex1]
   
   model
   {
      for( i in 1 : N )
      {
      x[i] ~ dinv.weib(beta, lambda)
      }
      
   #Prior distributions of the model parameters   
         beta ~ dunif(0, 10)
         lambda~ dunif(0, 2)   
   }

The data set is taken from Murthy et al. (2004, pp. 119).

Murthy, D. N. P., Xie, M., Jiang, R. (2004). Weibull Models, Wiley-Interscience.

MLE's are beta = 3.888075, lambda = 0.803668

Data
list(N=30,x=c(0.602, 0.603, 0.603, 0.615, 0.652, 0.663, 0.688, 0.705, 0.761, 0.770,
0.868, 0.884, 0.898, 0.901, 0.911, 0.918, 0.935, 0.953, 0.983, 1.009, 1.040,
1.097, 1.097, 1.148, 1.296, 1.343, 1.422, 1.540, 1.555, 1.653))
Inits for chain 1
list(beta=1.0, lambda=0.1)
   
Inits for chain 2
list(beta=7.0, lambda=1.0)



Results

[inv_weibull_ex2]

MAP estimates are
[inv_weibull_ex3]

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