STU34506 Modern Statistical Methods II

Module CodeSTU34506
Module Name Modern Statistical Methods II
ECTS Weighting [1]5 ECTS
Semester taughtSemester 2
Module Coordinator/s Alessio Benavoli

Module Learning Outcomes

Module Learning Outcomes 

On successful completion of this module, students will have the ability to

LO1. Devise suitable simulation methods for generating random numbers from a given probability distribution.

LO2. Use the sampled random numbers to estimate quantities of interest or evaluate integrals.

LO3. Assess the quality of the generated sample via diagnostic tools.

LO4. Choose appropriate Monte Carlo sampling algorithms.

LO5. Apply stochastic simulation methods in practice.

LO6. Modify the methods for a specific application.

Module Content

Monte Carlo methods are stochastic simulation-based algorithms designed to compute approximated solutions to problems where exact solutions are intractable and take exponential time to compute.  The scope of this module is to review fundamental aspects in Monte Carlo simulation.

Specific topics addressed in this module include:
• Random variable generation: transformation methods, accept-reject methods
• Monte Carlo integration and importance sampling
• Markov Chains Markov Chain Monte Carlo Methods, like Metropolis-Hastings and Gibbs sampler
• Sequential Monte Carlo Methods

• Theoretical aspects such as convergence and performance analysis

It also gives an opportunity for students to apply these tools to practical problems in statistical learning, data science, machine learning,

and other areas, using the R programming language.


Teaching and learning Methods

Live lecturers and QAs with accompanying lecture notes and handouts, available through Blackboard.
Blackboard discussion forums.

Assessment Details

Assessment ComponentBrief Description Learning Outcomes Addressed% of totalWeek setWeek Due
Examination2 hour Real-time examLO1, LO2, LO3, LO4, LO5, LO670%N/AN/A
AssignmentsFour assignments throughout the semesterLO1, LO2, LO3, LO4, LO5, LO630%3, 5, 7, 9

Reassessment Details

 Examination (2 hours, 100%) 

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by: 33 hours
Tutorial or seminar4
Independent study (outside scheduled contact hours), broken down by:82 hours
Preparation for classes and review of material (including preparation for examination, if applicable42
completion of assessments (including examination, if applicable)40
Total Hours115 hours

Recommended Reading List

Robert, Christian, and George Casella. “Monte Carlo statistical methods”. Springer Science & Business Media, 2013.
Robert, Christian , and George Casella. “Introducing Monte Carlo methods with R”. Vol. 18. New York: Springer, 2010.

Module Pre-requisites

Prerequisite modules: STU23501, STU22005

Other/alternative non-module prerequisites: Basic R programming and knowledge of linear algebra will be useful.

Module Co-requisites


Module Website