STAT40820 Monte Carlo (online)

Academic Year 2021/2022

This course aims to introduce a collection of powerful and computationally intensive modern Statistical methods. In particular this course will introduce concepts and methods involved in simulating from distributions. In turn this allows many familiar concepts such as point estimation, confidence intervals, maximum likelihood estimators to be computed. This course will introduce and make use of the free statistical software package R (www.r-project.org).

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Curricular information is subject to change

Learning Outcomes:

You will gain some understanding and knowledge of the techniques and tools which are available. The emphasis will be on understanding the principles behind he different algorithms. This course is not a course on Statistical computing, but you will understand and appreciate how to apply these methods in practice.

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities

24
Autonomous Student Learning

72
Online Learning

24
Total

120
Approaches to Teaching and Learning:
Lectures, tutorials, enquiry and problem-based learning. 
Requirements, Exclusions and Recommendations
Learning Recommendations:

Knowledge of Stochastic Processes and Bayesian Inference


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Online assessments Throughout the Trimester n/a Standard conversion grade scale 40% No

40
Examination: End of trimester written exam 2 hour End of Trimester Exam No Standard conversion grade scale 40% No

60

Carry forward of passed components
No
 
Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.