/* 

  Statistical dependence in Picat.

  From https://mhtess.github.io/bdappl/chapters/05-patterns.html

  Cf my Gamble model gamble_statistical_dependence.rkt

  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.

main => go.

/*

  obs = none
  var : a
  Probabilities:
  true: 0.5508999999999999
  false: 0.4491000000000000
  mean = [true = 0.5509,false = 0.4491]

  var : b
  Probabilities:
  true: 0.5009000000000000
  false: 0.4991000000000000
  mean = [true = 0.5009,false = 0.4991]

  var : c
  Probabilities:
  false: 0.5024999999999999
  true: 0.4975000000000000
  mean = [false = 0.5025,true = 0.4975]


  obs = false
  var : a
  Probabilities:
  false: 0.7971928397070789
  true: 0.2028071602929211
  mean = [false = 0.797193,true = 0.202807]

  var : b
  Probabilities:
  false: 1.0000000000000000
  mean = [false = 1.0]

  var : c
  Probabilities:
  false: 0.5048820179007323
  true: 0.4951179820992677
  mean = [false = 0.504882,true = 0.495118]


  obs = true
  var : a
  Probabilities:
  true: 0.8937839468919735
  false: 0.1062160531080266
  mean = [true = 0.893784,false = 0.106216]

  var : b
  Probabilities:
  true: 1.0000000000000000
  mean = [true = 1.0]

  var : c
  Probabilities:
  true: 0.5041239187286261
  false: 0.4958760812713739
  mean = [true = 0.504124,false = 0.495876]

*/
go ?=>
  member(Obs,[none,false,true]),
  println(obs=Obs),
  reset_store,
  run_model(10_000,$model(Obs),[show_probs_trunc,mean]),
  nl,
  % show_store_lengths,
  fail,
  nl.
go => true.
  
  
model(Obs) =>

  C = flip(0.5),
  B = flip(0.5),
  A = condt(B,flip(0.9),flip(0.2)),

  if Obs != none then
    observe(B == Obs),
    if observed_ok then
      add("a",A),
      add("b",B),
      add("c",C),
    end
  else
    add("a",A),
    add("b",B),
    add("c",C)
  end.