Dynamic Bayesian models are developed for application in nonlinear, non-normal time series and regression problems, providing dynamic extensions of standard generalized linear models. A key feature of the analysis is the use of conjugate prior and posterior distributions for the exponential family parameters. This leads to the calculation of closed, standard-form predictive distributions for forecasting and model criticism. The structure of the models depends on the time evolution of underlying state variables, and the feedback of observational information to these variables is achieved using linear Bayesian prediction methods. Data analytic aspects of the models concerning scale parameters and outliers are discussed, and some applications are provided.

Summary This paper describes a Bayesian approach to forecasting. The principles of Bayesian forecasting are discussed and the formal inclusion of “the forecaster” in the forecasting system is emphasized as a major feature. The basic model, the dynamic linear model, is defined together with the Kalman filter recurrence relations and a number of model formulations are given. Multi‐process models introduce uncertainty as to the underlying model itself, and this approach is described in a more general fashion than in our 1971 paper. Applications to four series are described in a sister paper. Although the results are far from exhaustive, the authors are convinced of the great benefits which the Bayesian approach offers to forecasters.

A new approach to short-term forecasting is described, based on Bayesian principles. The performance of conventional systems is often upset by the occurrence of changes in trend and slope, or transients. In this approach events of this nature are modelled explicitly, and successive data points are used to calculate the posterior probabilities of such events at each instant of time. The system produces not only single-figure forecasts but distributions of trend and slope values which are relevant to subsequent decisions based on forecasts.

Surgeons found this full procedural VR training module to be a realistic, feasible and acceptable component for a robotic surgical training programme. Construct validity was proven between expert and novice surgeons. Novice surgeons have shown a significant learning curve over 5.5 hours of training, suggesting this module could be used in a surgical curriculum for acquisition of technical skills. Further implementation of this module into the curriculum and continued analysis would be beneficial to gauge how it can be fully utilised.