## To instructors and students

This book is divided in two parts, foundations (chapters 1 to 5) and regression analysis (chapters 6 to 13). Our graphical user interface (GUI) targets the second part. This can be download at https://github.com/besmarter/BSTApp. Instructors and students can have all codes, simulated and real data sets there. There are two ways to install our GUI: First, install **docker**, and then type **docker pull magralo95/besmartergui:latest**, and **docker run –rm -p 3838:3838 magralo95/besmartergui** in your console (**cmd** also known as command prompt). This is our recommended installation due to stability. The second way is typing **shiny::runGitHub(“besmarter/BSTApp” , launch.browser=T)** in the R package console or any R code editor, and execute it.

Students should have some basic knowledge in probability theory and statistics, particularly, regression analysis. It is strongly recommended to have some familiarity with standard univariate and multivariate probability distributions.

I included some formal and computational exercises at the end of each chapter. This would help students to have a better understanding of the material shown in each chapter. Solutions of exercises are available at http://www.besmarter-team.org.

Instructors can use this book as a text in a course of introduction to Bayesian Econometrics with a high emphasis on implementation and applications. This book is complentary, rather than substitute, of excellent books in the topic ((Rossi, Allenby, and McCulloch 2012), (Greenberg 2012), (John Geweke 2005), (Lancaster 2004) and (Koop 2003)).

### References

*Contemporary Bayesian Econometrics and Statistics*. Vol. 537. John Wiley & Sons.

*Introduction to Bayesian Econometrics*. Cambridge University Press.

*Bayesian Econometrics*. John Wiley & Sons Inc.

*An Introduction to Modern Bayesian Econometrics*. Blackwell Oxford.

*Bayesian Statistics and Marketing*. John Wiley & Sons.