Lecturer: Simon Shaw, s.shaw@bath.ac.uk
Unit homepage: https://moodle.bath.ac.uk/course/view.php?id=1179
Lecture notes: http://people.bath.ac.uk/masss/ma40189/MA40189-notes.pdf
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Homework: There will be weekly question sheets handed out in the Thursday problems class. They should be submitted in the problems class on the following Thursday. The question sheets contain questions for submission and also extra questions which may be discussed in the problems class. The extra questions provide additional insight into both the course material and the questions for submission.
Feedback: Any work submitted by the hand-in deadline will be marked and returned, with personal feedback, to you. Full solutions to the extra questions will be published on moodle following the problems class in which they were discussed. Full solutions to all questions will be published on moodle immediately following the hand-in deadline with hard copies available in the corresponding problems class. General feedback sheets will be made available.
Office hours: I am happy to discuss any matters relating to the course at any time, either via email or one-to-one. If you would like to meet then just send me an email, with a list of proposed times and whether you wish to meet in-person or on Teams.
Assessment: 100% exam.
| Credits: | 6 |
| Level: | Masters |
| Period: | Semester 2 |
| Assessment: | Examination 100% |
| Other work: | There will be weekly question sheets. These will be set and handed in during problems classes. |
| Requisites: | Before taking this unit you must take MA40092. |
| Description: | Aims & Learning Objectives: |
| Aims: | |
| To introduce students to the ideas and techniques that underpin the theory and practice of the Bayesian approach to statistics. | |
| Objectives: | |
| Students should be able to formulate the Bayesian treatment and analysis of many familiar statistical problems. | |
| Content: | |
| Bayesian methods provide an alternative approach to data analysis, which has the ability to incorporate prior knowledge about a parameter of interest into the statistical model. The prior knowledge takes the form of a prior (to sampling) distribution on the parameter space, which is updated to a posterior distribution via Bayes’ Theorem, using the data. Summaries about the parameter are described using the posterior distribution. The Bayesian Paradigm; decision theory; utility theory; exchangeability; Representation Theorem; prior, posterior and predictive distributions; conjugate priors. Tools to undertake a Bayesian statistical analysis will also be introduced. Simulation based methods such as Markov Chain Monte Carlo and importance sampling for use when analytical methods fail. |