My talk will address a problem of fundamental importance for the Bayesian approach to evidence assessment in criminal cases. How shall a court, operating under the presumption of innocence, determine the prior probability that the defendant is guilty, before the evidence has been presented? I will examine some ways to approach this problem, and review different solutions. The considerations that determine the prior probability can be epistemic or normative. If they are purely epistemic, the fact that the defendant has been selected for prosecution must be considered as evidence for guilt, and this violates the presumption of innocence (Dawid 1993, 12). The prior probability must therefore be determined completely or partly on normative grounds. It has been suggested by Dennis Lindley and others that the prior probability shall be determined as 1/N, where N is the number of people who could have committed the act that the defendant is accused of (Lindley 1977, 218; Dawid 1993, 11; Bender & Nack 1995, 236), but there are several objections to this solution. As Leonard Jaffee has pointed out, the prior probability will not be equal in all criminal trials, as N will vary from case to case (Jaffee 1988, 978). This is problematic since the doctrine of fair trial requires that defendants are treated equally. Furthermore, the court will not have sufficient knowledge about all possible scenarios to determine N with the robustness required by the standard of proof (Dahlman, Wahlberg & Sarwar 2015, 19). My suggestion for the problem is that the prior probability should be determined completely on normative grounds, by assigning a standardized number to N, for example N = 100. If the number of people who could have committed the crime is always presumed to be 100, the probability that the defendant is guilty before the evidence has been presented will be 1% in all trials. According to this solution, the prior probability is an institutional fact (Searle 1995, 104).