2005 (UTC)

I wrote the paragraph quoting Lindley that Peter finds puzzling. Bayes' theorem can be expressed in the form

Posterior probability = Prior probability x Likelihood

If the prior is zero then so is the posterior, since zero times anything equals zero. A similar argument applies if the pror is 1. The likelihood is the part which mathematically models the information content of the data. In the case where the prior is zero it makes no difference what the likelihood is, since it just gets multiplied by zero to make zero. So by choosing a prior probability of zero (or one) you cut yourself off from the ability to take on board the informaton contained in the data. I hope this helps. 23:21, 19 September