start with pfl learning.

packages/CLPBN/examples/learning/sprinkler_params.yap

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+% learn distribution for school database.
+
+:- ['../sprinkler.pfl'].
+
+:- use_module(library(clpbn/learning/em)).
+
+%% data(t,t,t,t).
+data(t,f,_,t).
+%% data(_,t,_,t).
+%% data(t,t,f,f).
+%% data(t,t,f,t).
+%% data(t,_,_,t).
+%% data(t,f,t,t).
+%% data(t,t,f,t).
+%% data(t,_,f,f).
+%% data(t,t,f,f).
+%% data(f,f,t,t).
+%% data(t,t,_,f).
+%% data(t,f,f,t).
+%% data(t,f,t,t).
+
+%:- clpbn:set_clpbn_flag(em_solver,gibbs).
+%:- clpbn:set_clpbn_flag(em_solver,jt).
+%:- clpbn:set_clpbn_flag(em_solver,ve).
+:- clpbn:set_clpbn_flag(em_solver,bp).
+
+timed_main :-
+	statistics(runtime, _),
+	main(Lik),
+	statistics(runtime, [T,_]),
+	format('Took ~d msec and Lik ~3f~n',[T,Lik]).
+
+main(Lik) :-
+	findall(X,scan_data(X),L),
+        em(L,0.01,10,_,Lik).
+
+scan_data(example([wet_grass(W),sprinkler(S),rain(R),cloudy(C)])) :-
+	data(W, S, R, C).
+
+

packages/CLPBN/examples/sprinkler.pfl

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+
+:- style_check(all).
+
+:- ensure_loaded(library(pfl)).
+
+% 1. define domain of random variables
+%    not necessary if they are boolean.
+
+% 2. define parfactors
+
+bayes cloudy ; cloudy_table ; [].
+
+bayes sprinkler, cloudy ; sprinkler_table ; [].
+
+bayes rain, cloudy ; rain_table ; [].
+
+bayes wet_grass, sprinkler, rain ; wet_grass_table ; [].
+
+
+% 3. define CPTs.
+
+wet_grass_table([1.0,0.1,0.1,0.01,
+                 0.0,0.9,0.9,0.99]).
+
+sprinkler_table([0.5,0.9,
+		 0.5,0.1]).
+
+rain_table([0.8,0.2,
+	    0.2,0.8]).
+
+cloudy_table([0.5,0.5]).
+
+