In many applications data is collected over time or can be ordered with respect to some other criteria (e.g. position along a chromosome). Often the statistical properties, such as mean or autocovariance, of the data will change across the data. This feature of data is known as non-stationarity. An important and challenging problem is to be able to model and infer how these properties change.
Two possibilities for modelling non-stationarity are change-point models and locally-stationary models. One of the goals of the workshop is to investigate and develop links between these two approaches.
This workshop will start with overview lectures that introduce change-point and locally-stationary models and approaches to inference for these models.
There are opportunities for participants to present their work as a talk or poster. If you wish to do so, please include a short abstract (max 1 page) with your application.