  Dynamic linear models (DLM) offer a very generic framework to analyse time
series data. Many classical time series models can be formulated as DLMs,
including ARMA models and standard multiple linear regression models. The
models can be seen as general regression models where the coefficients can vary
in time. In addition, they allow for a state space representation and a
formulation as hierarchical statistical models, which in turn is the key for
efficient estimation by Kalman formulas and by Markov chain Monte Carlo (MCMC)
methods. A dynamic linear model can handle non-stationary processes, missing
values and non-uniform sampling as well as observations with varying
accuracies. This chapter gives an introduction to DLM and shows how to build
various useful models for analysing trends and other sources of variability in
geodetic time series.
