 must be at least \(1-(\psi /n)\), for some explicitly calculable term \(\psi\). Thus, the true hypothesis \(h_i\) probabilistically implies that as the amount of evidence, n, increases, it becomes highly likely (as close to 1 as you please) that one of the outcome sequences \(e^n\) will occur that yields a likelihood ratio \(P[e^n \pmid h_{j}\cdot b\cdot c^{n}] / P[e^n \pmid h_{i}\cdot b\cdot c^{n}]\) less than \(\varepsilon\); and this holds for any specific value of \(\varepsilon\) you may choose. As this happens, the posterior probability of \(h_i\)’s false competitor, \(h_j\), must approach 0, as required by the Ratio Form of Bayes’ Theorem,  Equation 9*.