STAT40400 Monte Carlo Inference

Academic Year 2021/2022

Computers have to a large extent changed what is now possible for Statistics. A rough classifiction of the uses of computers in modern Statistics might be: Graphical data exploration; data modelling; inference. This course focuses exclusively on inference. It 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 aply these methods in practice.

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities

40

Autonomous Student Learning

55

Lectures

18

Computer Aided Lab

6

Total

119

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

Basic course in statistics including probability, inference, hypothesis testing

Learning Recommendations:

Knowledge of Stochastic Processes, Bayesian Inference


Module Requisites and Incompatibles
Incompatibles:
STAT40410 - Monte Carlo Inference


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: End of trimester written exam 2 hour End of Trimester Exam No Standard conversion grade scale 40% No

60

Assignment: Assignments Varies over the Trimester n/a Standard conversion grade scale 40% No

40


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.

Name Role
Mr Chaoyi Lu Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Autumn
     
Lecture Offering 1 Week(s) - Autumn: All Weeks Fri 11:00 - 11:50
Lecture Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 Wed 15:00 - 15:50
Lecture Offering 1 Week(s) - 12 Wed 15:00 - 15:50
Computer Aided Lab Offering 1 Week(s) - 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 Mon 16:00 - 16:50
Computer Aided Lab Offering 2 Week(s) - 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 Tues 12:00 - 12:50
Autumn