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Dear Bugs Users,

I am modeling a bearing lifetime in WinBUGS 1.4.1.
There are 5 units on test. They are told by the bearing manufacturer that
the bearings should last 100 hours (L10 life) in this application. L10 life
means the time at which 10 % of the population fail. Thus far one unit has
failed at 40 hours, one at 8 hours.  One unit has 13 hours with no failures,
one has 15 hours with no failures, and one has 1 hour with no failures. I'm
trying to set up a Bayesian updating model that calculates the probability
of the bearing life falling within a given range. An initial distribution of
equal probabilities is suggested that the bearing life is 20-40 hours, 40-60
hours, 60-80 hours, 80-100 hours, and 100-120 hours.
I'm not sure the model below is reasonable. Can anyone give some comments
to me?

Thanks in advance,

Yoonik.

model
{
 for(i in 1 : M) {
  for(j in 1 : N) {
   t[i, j] ~ dexp(lambda.new[i])I(t.cen[i, j],)
  }
 lambda[i] ~ dunif(0, 949.1222)
 lambda.new[i] <- lambda[i]
 life[i] <- 1/lambda.new[i]
 }
}


list(t = structure(.Data =
      c(40, 8, NA, NA, NA),
   .Dim = c(1, 5)),
  t.cen = structure(.Data =
      c(  0,   0,  13, 15, 1),
   .Dim = c(1, 5)),
  M = 1, N = 5)

list(lambda = c(0.00105))






-- 
---------------------------------
Yoonik Kim, Ph.D.

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