In our experiment, sampling from truncated normal distributions is realized by using the idea of rejection sampling: to sample from a truncated nor- we first find an envelope density function g(x) that is easy to sample directly, such that π c (x) is uniformly bounded by M • g(x) for some constant M that does not depend on x.
even though the expected model assigns equal values to µ 3 and µ 4 (corresponding to D and B, respectively), µ 3 is always less than µ 4 , simply because there is more chance of penalizing D rather than B.