My PhD, online

• Contents
• Introduction
• Bayes theorem and its implications
• Recursive Bayesian Estimation, one dependent and one explanatory variable
• Recursive Bayesian Estimation, one dependent and several explanatory variables
• Recursive Bayesian Estimation, one dependent and one explanatory variable; some results
• Recursive Bayesian Estimation, several dependent and several explanatory variables
• Past applications of the Kalman Filter
• The benefits of Kalman Filter models compared to conventional methods; a Monte Carlo study
• A Kalman Filter estimated model of energy demand; one dependent and several explanatory variables
• A Kalman Filter estimated model of wool demand; multiple dependent and several explanatory variables
• Part 2
• Part 3
• Ideas for further work
• Summary, discussion, main conclusions and recommendations
• Abbreviations and notation
• The Kalman Filter program
• The Kalman Filter
• The recursive Bayesian estimation programs on the Texas Instruments programmable calculator.
• References.