 The probability of a hypothesis given multiple pieces of evidence can, in principle, be assessed by sequential applications of Bayes’ theorem. Consider, for example, a case in which the defendant faces two pieces of incriminating evidence: a DNA match and hair evidence found at the scene matching the defendant’s hair color. Assume—as is often done—that someone’s hair color is independent of someone’s genetic profile. Say the likelihood ratio of hair evidence is 40 and the likelihood ratio of the DNA match is 200, that is, it is 40 times more likely (and 200 times more likely) to find the evidence given the guilt hypothesis than the innocence hypothesis. If the prior odds of guilt to innocence are \(\slashfrac{1}{1000}\), the posterior odds would be \(\slashfrac{1}{1000}\times 40\times 200=8\).