 We assume that, for each pair of settings \(a, b\), and every \(\lambda\) in \(\Lambda\), there is a probability function \(p_{a,b}(s,t \mid \lambda)\), which takes on values in the interval [0, 1] and sums to unity when summed over all \(s\) in \(S_a\) and \(t\) in \(T_b\). These response functions may include implicit averaging over possible states of the experimental apparatus.[4] We can use these probability functions — which we will call response probabilities — to define marginal probabilities: