/* 

  Log gamma distribution in Picat.

  See more in ppl_distributions.pi and ppl_distributions_test.pi

  Cf my Gamble model gamble_log_gamma_dist.rkt
  Note: My Gamble implementation has parameter in different
        order.

  This program was created by Hakan Kjellerstrand, hakank@gmail.com
  See also my Picat page: http://www.hakank.org/picat/

*/

import ppl_distributions, ppl_utils.
import util.
% import ordset.

main => go.

/*
  var : d
  Probabilities (truncated):
  7.230093711237946: 0.0001000000000000
  6.980156980411966: 0.0001000000000000
  6.846738128102021: 0.0001000000000000
  5.829432765039231: 0.0001000000000000
  .........
  2.000137930562389: 0.0001000000000000
  2.00006881169998: 0.0001000000000000
  2.000020700721352: 0.0001000000000000
  2.000002653498838: 0.0001000000000000
  mean = 2.25473
  variance = 0.106087

  [min = 2.0,mean = 2.25473,median = 2.15097,max = 7.23009,variance = 0.106087,stdev = 0.325711]


  Picat> X=log_gamma_dist_mean(1,0.2,2)
  X = 2.25

  Picat> X=log_gamma_dist_variance(1,0.2,2)
  X = 0.104166666666667

  % Stdev
  Picat> X=log_gamma_dist_variance(1,0.2,2).sqrt
  X = 0.322748612183952



*/
go ?=>
  reset_store,
  run_model(10_000,$model,[show_probs_trunc,mean,variance % ,
                           % show_percentiles,
                           % show_hpd_intervals,hpd_intervals=[0.94],
                           % show_histogram,
                           % min_accepted_samples=1000,show_accepted_samples=true
                          ]),
  Post = get_store().get("d"),
  show_simple_stats(Post),
  % show_store_lengths,nl,
  % fail,
  nl.
go => true.
  
  
model() =>
  D = log_gamma_dist(1,0.2,2),  
  
  add("d",D).