Andrés Ramírez-Hassan is a Distinguished Professor at Universidad EAFIT whose work advances Bayesian econometrics and applied statistical modeling. His research has appeared in journals such as the Journal of Applied Econometrics, Econometric Reviews, and the International Journal of Forecasting. He has served as a researcher and consultant for global institutions, including the United Nations Development Programme and the Inter-American Development Bank. He was a Research Fellow in the Department of Econometrics and Business Statistics at Monash University, and a Visiting Professor at the University of Melbourne.

• Part I: Foundations: Theory, simulation methods and programming. 1. Basic formal concepts. 2. Conceptual differences between the Bayesian and Frequentist approaches. 3. Cornerstone models: Conjugate families. 4. Simulation methods. Part II: Regression models: A GUIded toolkit. 5. Graphical user interface. 6. Univariate models. 7. Multivariate models. 8. Time series models. 9. Longitudinal/Panel data models. 10. Bayesian model averaging. Part III: Advanced methods: A brief introduction. 11. Semi-parametric and non-parametric models. 12. Bayesian machine learning. 13. Causal inference. 14. Approximate Bayesian methods.

Andrés Ramírez-Hassan is a Distinguished Professor at Universidad EAFIT whose work advances Bayesian econometrics and applied statistical modeling. His research has appeared in journals such as the Journal of Applied Econometrics, Econometric Reviews, and the International Journal of Forecasting. He has served as a researcher and consultant for global institutions, including the United Nations Development Programme and the Inter-American Development Bank. He was a Research Fellow in the Department of Econometrics and Business Statistics at Monash University, and a Visiting Professor at the University of Melbourne.

• Part I: Foundations: Theory, simulation methods and programming. 1. Basic formal concepts. 2. Conceptual differences between the Bayesian and Frequentist approaches. 3. Cornerstone models: Conjugate families. 4. Simulation methods. Part II: Regression models: A GUIded toolkit. 5. Graphical user interface. 6. Univariate models. 7. Multivariate models. 8. Time series models. 9. Longitudinal/Panel data models. 10. Bayesian model averaging. Part III: Advanced methods: A brief introduction. 11. Semi-parametric and non-parametric models. 12. Bayesian machine learning. 13. Causal inference. 14. Approximate Bayesian methods.

Andrés Ramírez-Hassan is a Distinguished Professor at Universidad EAFIT whose work advances Bayesian econometrics and applied statistical modeling. His research has appeared in journals such as the Journal of Applied Econometrics, Econometric Reviews, and the International Journal of Forecasting. He has served as a researcher and consultant for global institutions, including the United Nations Development Programme and the Inter-American Development Bank. He was a Research Fellow in the Department of Econometrics and Business Statistics at Monash University, and a Visiting Professor at the University of Melbourne.

• Part I: Foundations: Theory, simulation methods and programming. 1. Basic formal concepts. 2. Conceptual differences between the Bayesian and Frequentist approaches. 3. Cornerstone models: Conjugate families. 4. Simulation methods. Part II: Regression models: A GUIded toolkit. 5. Graphical user interface. 6. Univariate models. 7. Multivariate models. 8. Time series models. 9. Longitudinal/Panel data models. 10. Bayesian model averaging. Part III: Advanced methods: A brief introduction. 11. Semi-parametric and non-parametric models. 12. Bayesian machine learning. 13. Causal inference. 14. Approximate Bayesian methods.