Graduate course.
This course aims to introduce to the students the fundamentals of statistics through the use of computational tools. We motivate the formulation of statistical inference (point estimates, interval estimates and hypothesis testing) as part of the endeavors an applied statistician is going to solve in their professional career. That is, we motivate statistics in the context of decision-making under uncertainty.
We cover frequentist statistics with the help of resampling techniques in order to motivate the clear distinction of the assumptions under this framework. Later on, we cover the basics of Bayesian inference under a decision making process.
The course is followed up by the course in bayesian modeling where specifics on probabilistic programming and approximate methods are studied in the context of inference and machine learning.
This course co-developed with Teresa Ortiz and Felipe Gonzalez. It has been instructed for the Msc in Data Science, Msc in Computational Sciences and Msc in Applied Economics. Lecture notes are available here (in Spanish).
- Tags
- inference, R, tidyverse, resampling, bootstrap