## Admission requirements

It is highly recommended that students have completed the courses “Statistical computing with R” and “Statistics and probability” from the MSc in Statistics and Data Science before following this course.

## Description

This course will provide an introduction to advanced methods for statistical computing. It will cover topics such as methods for the generation of random variables, optimization and numerical integration techniques, the Expectation Maximization algorithm, and Monte Carlo methods.

## Course objectives

The goals of the course are to familiarize students with advanced statistical computing methods and to provide them with the programming skills needed to implement them.

## Mode of instruction

The course will consist of a combination of lectures where the computing methods are introduced, and computer practicals where students will use R to implement these methods.

## Assessment method

The final grade will be determined as a weighted average of the homework assigned during the course (30%) and the final (retake) exam (70%).

The homework counts as a practical and there is no retake for it.

The final grade should be at least 5.5 (which will be rounded to 6) to get a pass.

## Literature

Statistical computing with R. Maria Rizzo, CRC Press (2nd edition, 2019).

## Brightspace/website

Brightspace

## Contact information

advstatcomp[at]gmail.com