Estimating the probability of event occurrence

Sammanfattning: In complex systems anomalous behaviors can occur intermittently and stochastically. In this case, it is hard to diagnose real errors among spurious ones. These errors are often hard to troubleshoot and require close attention, but troubleshooting each occurrence is time-consuming and is not always an option. In this thesis, we define two different models to estimate the underlying probability of occurrence of an error, one based on binary segmentation and null hypothesis testing, and the other one based on hidden Markov models. Given a threshold level of confidence, these models are tuned to trigger alerts when a change is detected with sufficiently high probability. We generated events drawn from Bernoulli distributions emulating these anomalous behaviors to benchmark these two candidate models. Both models have the same sensitivity, δp ≈ 10%, and delay, δt ≈ 100 observations, to detect change points. However, they do not generalize in the same way to broader problems and provide therefore two complementary solutions.