161308

Bayesian Computational Statistics: Towards Causal Thinking

This course covers the ideas underlying statistical modelling in science through the lens of causal thinking. We cover the implementation of these ideas through Bayesian computational methods and links to practical applications including those in Ecology, Genetics, Nutrition Science and Psychology.
Course code

Qualifications are made up of courses. Some universities call these papers. Each course is numbered using six digits.

161308
Level

The fourth number of the course code shows the level of the course. For example, in course 219206, the fourth number is a 2, so it is a 200-level course (usually studied in the second year of full-time study).

300-level
Credits

Each course is worth a number of credits. You combine courses (credits) to meet the total number of credits needed for your qualification.

15
Subject
Statistics

Course planning information

Course notes

Topics include causality, Bayesian statistics, multi-level -aka hierarchical- modelling, generalised linear models and missing data.

Prerequisite courses

Complete first
(160101 or 160102 or 160105) and (161220 or 161221 or 161250 or 161251)

You need to complete the above course or courses before moving onto this one.

Restrictions

Similar content

You cannot enrol in this course if you have passed (or are enrolled in) any of the course(s) above as these courses have similar content or content at a higher level.

General progression requirements

You must complete at least 45 credits from 200-level before enrolling in 300-level courses.

Learning outcomes

What you will learn. Knowledge, skills and attitudes you’ll be able to show as a result of successfully finishing this course.

  • 1 Apply the concepts of Bayesian statistics and causality to a variety of situations
  • 2 Analyse causal relationships between variables, use of causal diagrams
  • 3 Write computer programs to fit models to data
  • 4 Choose appropriate candidate models for a given situation
  • 5 Perform simulation studies to evaluate stochastic models

Learning outcomes can change before the start of the semester you are studying the course in.

Assessments

Assessment Learning outcomes assessed Weighting
Written Assignment 1 2 3 20%
Written Assignment 1 2 3 4 20%
Test 1 2 4 10%
Written Assignment 1 2 3 4 5 40%
Oral/Performance/Presentation 1 2 3 4 5 10%

Assessment weightings can change up to the start of the semester the course is delivered in.

You may need to take more assessments depending on where, how, and when you choose to take this course.

Explanation of assessment types

Explanation of assessment types
Computer programmes
Computer animation and screening, design, programming, models and other computer work.
Creative compositions
Animations, films, models, textiles, websites, and other compositions.
Exam College or GRS-based (not centrally scheduled)
An exam scheduled by a college or the Graduate Research School (GRS). The exam could be online, oral, field, practical skills, written exams or another format.
Exam (centrally scheduled)
An exam scheduled by Assessment Services (centrally) – you’ll usually be told when and where the exam is through the student portal.
Oral or performance or presentation
Debates, demonstrations, exhibitions, interviews, oral proposals, role play, speech and other performances or presentations.
Participation
You may be assessed on your participation in activities such as online fora, laboratories, debates, tutorials, exercises, seminars, and so on.
Portfolio
Creative, learning, online, narrative, photographic, written, and other portfolios.
Practical or placement
Field trips, field work, placements, seminars, workshops, voluntary work, and other activities.
Simulation
Technology-based or experience-based simulations.
Test
Laboratory, online, multi-choice, short answer, spoken, and other tests – arranged by the school.
Written assignment
Essays, group or individual projects, proposals, reports, reviews, writing exercises, and other written assignments.

Textbooks needed

There are no set texts for this course.