 Let’s consider a simple example of how the Ratio Form of Bayes’ Theorem applies to a collection of independent evidential events. Suppose we possess a warped coin and want to determine its propensity for heads when tossed in the usual way. Consider two hypotheses, \(h_{[p]}\) and \(h_{[q]}\), which say that the propensities for the coin to come up heads on the usual kinds of tosses are \(p\) and \(q\), respectively. Let \(c^n\) report that the coin is tossed n times in the normal way, and let \(e^n\) report that precisely m occurrences of heads has resulted. Supposing that the outcomes of such tosses are probabilistically independent (asserted by \(b\)), the respective likelihoods take the binomial form 