count =

     7

outline
basic parameters

p =

    0.3000    0.0500    0.1000

simulating fails per month
sampling from the prior distribution
samping from the posterior using importance sampling
computing weights

s =

   1.6911e-14

sort and display results
true -- lower - expected - upper for lambda1

ans =

    0.3000    0.0385    0.2680    0.4004

true -- lower - expected - upper for lambda2

ans =

    0.0500    0.0434    0.1277    0.2735

true -- lower - expected - upper for alpha

ans =

    0.1000    0.2033    5.1884   14.1712

posterior predictive

p =

    0.3000    0.0500    0.1000

#failed in month I+1, #predicted, 2*SD

ans =

  702.0000  517.4653   43.3308

cross validation

i =

     3

sampling from prior (again)
compute the weights for these prior samples
computing weights

s =

   6.2274e-09

cross val values

x_post_sim_cross =

    0.1143    0.2591    2.5483

obs(c) - expected value 2*SD

ans =

  494.0000  449.5860   37.3261


i =

     4

sampling from prior (again)
compute the weights for these prior samples
computing weights

s =

   2.4661e-09

cross val values

x_post_sim_cross =

    0.1232    0.2285    0.7527

obs(c) - expected value 2*SD

ans =

  562.0000  559.5893   42.5247


i =

     5

sampling from prior (again)
compute the weights for these prior samples
computing weights

s =

   1.8310e-08

cross val values

x_post_sim_cross =

    0.2936    0.0868    0.1777

obs(c) - expected value 2*SD

ans =

  700.0000  664.1273   46.6821


i =

     6

sampling from prior (again)
compute the weights for these prior samples
computing weights

s =

   3.3480e-12

cross val values

x_post_sim_cross =

    0.1149    0.2176    0.6146

obs(c) - expected value 2*SD

ans =

  701.0000  684.0671   48.1809

>>