Doob (1949) obtained a very general result on the consistency of Bayes' estimates. Loosely, if any consistent estimates are available, then the Bayes' estimates are consistent for almost all values of the parameter under the prior measure. If the parameter is thought of as being selected by nature through a random mechanism whose probability law is known, Doob's result is completely satisfactory. On the other hand, in some circumstances it is necessary to identify the exceptional null set. For example, if the parameter is thought of as fixed but unknown, and the prior measure is chosen as a convenient way to calculate estimates, it is important to know for which null set the method fails. In particular, it is desirable to choose the prior so that the null set is in fact empty. The problem is very delicate; considerable work [8], [9], [12] has been done on it recently, in quite general contexts and under severe regularity assumptions. It might therefore be of interest to discuss the simplest possible case, that of independent, identically distributed, discrete observations, in some detail. This will be done in Sections 3 and 4 when the observations take a finite set of possible values. Under this assumption, Section 3 shows that the posterior probability converges to point mass at the true parameter value among almost all sample sequences (for short, the posterior is consistent; see Definition 1) exactly for parameter values in the topological carrier of the prior. In Section 4, the asymptotic normality of the posterior is shown to follow from a local smoothness assumption about the prior. In both sections, results are obtained for priors which admit the possibility of an infinite number of states. The results of these sections are not entirely new; see pp. 333 ff. of [7], pp. 224 ff. of [10], [11]. They have not appeared in the literature, to the best of our knowledge, in a form as precise as Theorems 1, 3, 4. Theorem 2 is essentially the relevant special case of Theorem 7.4 of Schwartz (1961). In Sections 5 and 6, the case of a countable number of possible values is treated. We believe the results to be new. Here the general problem appears, because priors which assign positive mass near the true parameter value may lead to ridiculous estimates. The results of Section 3 (let alone 4) are false. In fact, Theorem 5 of Section 5 gives the following construction. Suppose that under the true parameter value the observations take an infinite number of values with positive probability. Then given any spurious (sub-)stochastic probability distribution, it is possible to find a prior assigning positive mass to any neighborhood of the true parameter value, but leading to a posterior probability which converges for almost all sample sequences to point mass at the spurious distribution. Indeed, there is a prior assigning positive mass to every open set of parameters, for which the posterior is consistent only at a set of parameters of the first category. To some extent, this happens because at any stage information about a finite number of stages only is available, but on the basis of this evidence, conclusions must be drawn about all states. If the prior measure has a serious prejudice about the shape of the tails, disaster ensues. In Section 6, it is shown that a simple condition on the prior measure (which serves to limit this prejudice) ensures the consistency of the posterior. Prior probabilities leading to posterior distributions consistent at all and asymptotically normal at essentially all (see Remark 3, Section 3) parameter values are constructed. Section 5 is independent of Sections 3 and 4; Section 6 is not. Section 6 overlaps to some extent with unpublished work of Kiefer and Wolfowitz; it has been extended in certain directions by Fabius (1963). The results of this paper were announced in [5]; some related work for continuous state space is described in [3]. It is a pleasure to thank two very helpful referees: whatever expository merit Section 5 has is due to them and to L. J. Savage.