STAT20180 Bayesian Analysis

Academic Year 2020/2021

Bayesian Analysis models all unknown quantities in a coherent probabilistic framework. Full probability distributions for model parameters conditional on observed data are derived. This module explores how this can be done, both algebraically and computationally. Understanding the Bayesian approach to inference is central and manipulation of conditional distributions is key. The free software package JAGS will be used to perform analysis on a range of statistical models, from simple to complex hierarchical models. Topics covered include: conditional probability, Bayes' Theorem, prior distributions, conjugacy, multi-parameter problems, Bayesian hypothesis testing and model checking, methods for finding the posterior mode, Markov Chain Monte Carlo. Illustrative examples from the scientific literature will be used.

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Curricular information is subject to change

Learning Outcomes:

By the end of the course the students should be able to propose and fit a fully Bayesian statistical model to a wide variety of data sets. They should be able to check the model and give a critique of the Bayesian process as opposed to its Frequentist counterpart.

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Tutorial

5
Computer Aided Lab

5
Specified Learning Activities

10
Autonomous Student Learning

75
Total

119
Approaches to Teaching and Learning:
Video lectures posted each week that walk through module content, blending theory with example exercises. Interactive lecture sessions once a week for each student, delivered in the Virtual Classroom.
Interactive live tutorials in smaller groups to work on practical problems. Sample solutions for these will be posted approximately one week after each problem set. Coding based problem sets delivered in the virtual classroom with solutions again following.
Worked solutions to assignments posted after each deadline.
All content delivered using the VLE which includes a monitored discussion forum with topics created for each weeks lecture material and each problem set. 
Requirements, Exclusions and Recommendations
Learning Recommendations:

You should have completed a basic course in statistics including probability, inference, hypothesis testing, estimation and regression.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: End of trimester exam. 2 hour End of Trimester Exam No Graded No

70
Continuous Assessment: Assignments will be a mix of theory and computer based problem sheets. Throughout the Trimester n/a Graded No

30

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

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

A First Course in Bayesian Statistical Methods by Peter D. Hoff.
Bayesian Statistics: An Introduction by Peter M. Lee.