Cumulative-response logistic model
= Cumulative-response logistic model
{title2=$\operatorname{logit}(p_t)=x^T\beta+\gamma\sum_{s<t}Y_s$}
The accumulated number of previous successes may enter the conditional mean in a <history-dependent logistic regression>. Its coefficient exponentiates to the conditional success <odds ratio> per previous success. For a specified future path, update this cumulative predictor after each outcome before multiplying the conditional <probabilities>.