Experience with classical statistical methods.

The main course objectives are: 1. Understand fundamental concepts of probability relevant to Bayesian Statistics. 2. Compare and contrast the Frequentist and Bayesian approaches to statistical inference. 3. Learn about conjugate distributions and their significance in Bayesian analysis. 4. Develop proficiency in computational methods used for Bayesian estimation. 5. Gain knowledge in Regression and Linear Models within a Bayesian framework. 6. Explore Generalized Linear Models (GLMs) from a Bayesian perspective. 7. Study Bayesian clustering techniques for data analysis and pattern discovery. 8. Learn about Nonlinear Regression and its application in Bayesian modeling.

1. Basic concepts of probability related with Bayesian Statistics 2. Frequentist vs. Bayesian approach 3. Conjugate distributions 4. Computational methods for Bayesian estimation 5. Regression and Linear Models 6. Generalized linear models 7. Bayesian clustering 8. Non linear regression

Learning activities: Theoretical lessons Exercises Practical lessons in class using the computer Individual project Teaching methodologies: Presentations of the professor in class with computing and visual media, where the professor develops the mail concepts of the subject and provides bibliography supplementing the knowledge of students. Solving practical case studies, problems, etc., proposed by the professor either individually or in groups