Efficient Bayesian detection of multiple events with a minimum-distance constraint

We propose a Bayesian method for detecting multiple events in signals under the practically relevant assumption that successive events may not be arbitrarily close and distant events are effectively independent. Our detector has low complexity since it involves only the (Monte Carlo approximation to the) one-dimensional marginal posteriors. However, its performance is good since the metric it minimizes depends on the entire event sequence. We also describe an efficient sequential implementation of our detector that is based on a tree representation and a recursive metric computation.