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Hi Vincent These should give you the same answer. However, (2) will be preferable in cases where you want to specify this constraint at run-time without the model getting recompiled - you can set the constraint to positive or negative just by setting the observed value of b. I have used this to good...
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The subtlety here is that ConstrainEqualRandom(new Bernoulli(0.6)) does not constrain the posterior over P to be Bernoulli(0.6). Instead it modifies the posterior so that the probability of P being true is weighted by 0.6 and the probability of P being false is weighted by 0.4 – the probabilities...