Introduction: basic notions about Bayesian inference

Abstract

Dynamic linear models were developed in engineering in the early 1960’s, to monitor and control dynamic systems, although pioneer results can be found in the statistical literature and go back to Thiele (1880). Early famous applications have been in the Apollo and Polaris aerospace programs (see, e.g., Hutchinson; 1984), but in the last decades dynamic linear models, and more generally state space models, have received an enormous impulse, with applications in an extremely vast range of fields, from biology to economics, from engineering and quality control to environmental studies, from geophysical science to genetics. This impressive growth of applications is largely due to the possibility of solving computational difficulties using modern Monte Carlo methods in a Bayesian framework. This book is an introduction to Bayesian modeling and forecasting of time series using dynamic linear models, presenting the basic concepts and techniques, and illustrating an R package for their practical implementation.