small fixes

examples/inference/arithm.pl

@@ -47,11 +47,9 @@
 
 identity(_):0.5.
 
+random_const(_,C):discrete(C,[0:0.1,1:0.1,2:0.1,3:0.1,4:0.1,
+  5:0.1,6:0.1,7:0.1,8:0.1,9:0.1]).
 
-random_const(L,0):0.1;random_const(L,1):0.1;random_const(L,2):0.1;
-random_const(L,3):0.1;random_const(L,4):0.1;random_const(L,5):0.1;
-random_const(L,6):0.1;random_const(L,7):0.1;random_const(L,8):0.1;
-random_const(L,9):0.1.
 
 
 

examples/inference/kalman_filter.pl

@@ -5,7 +5,7 @@
 and the output is given by the current state plus Gaussian noise (mean
 0 and variance 1 in this example). 
 This example can be considered as modeling a random walk of a single continuous 
-state variable with a noisy observation. 
+state variable with noisy observations. 
 Given that at time 0 the value 2.5 was
 observed, what is the distribution of the state at time 1 (filtering problem)?
 The distribution of the state is plotted in the case of having (posterior) or 

examples/inference/slp_pcfg.pl

@@ -29,22 +29,20 @@
 :- begin_lpad.
 
 s_as(N):0.2;s_bs(N):0.2;s_a(N):0.3;s_b(N):0.3.
-s([a|R],N0,N):-
+s([a|R],N0):-
   s_as(N0),
   N1 is N0+1,
-  s(R,N1,N).
-s([b|R],N0,N):-
+  s(R,N1).
+s([b|R],N0):-
   s_bs(N0),
   N1 is N0+1,
-  s(R,N1,N).
-s([a],N0,N):-
-  s_a(N0),
-  N is N0+1.
-s([b],N0,N):-
-  s_b(N0),
-  N is N0+1.
-
-s(L):-s(L,0,_).
+  s(R,N1).
+s([a],N0):-
+  s_a(N0).
+s([b],N0):-
+  s_b(N0).
+
+s(L):-s(L,0).
 :- end_lpad.
 
 /** <examples>