A hierarchical model is developed to account for selection biases that result from processes in which events have a fixed probability of being sampled, but individuals in the population generate events at varying rates. It is shown that inferences about the population parameters from such...

One important implementation of Bayesian forecasting is the Multi-State Kalman Filter (MSKF) method. It is particularly suited for short and irregular time series data. In certain applications, time series data are available on numerous parallel observational units which, while not having...